diff --git a/modules/cudaobjdetect/include/opencv2/cudaobjdetect.hpp b/modules/cudaobjdetect/include/opencv2/cudaobjdetect.hpp
index 32126515a7..2887bf472c 100644
--- a/modules/cudaobjdetect/include/opencv2/cudaobjdetect.hpp
+++ b/modules/cudaobjdetect/include/opencv2/cudaobjdetect.hpp
@@ -65,32 +65,24 @@ namespace cv { namespace cuda {
 // HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector
 //
 
-struct CV_EXPORTS HOGConfidence
-{
-   double scale;
-   std::vector<Point> locations;
-   std::vector<double> confidences;
-   std::vector<double> part_scores[4];
-};
-
 /** @brief The class implements Histogram of Oriented Gradients (@cite Dalal2005) object detector.
 
-Interfaces of all methods are kept similar to the CPU HOG descriptor and detector analogues as much
-as possible.
-
 @note
-   -   An example applying the HOG descriptor for people detection can be found at
+    -   An example applying the HOG descriptor for people detection can be found at
         opencv_source_code/samples/cpp/peopledetect.cpp
     -   A CUDA example applying the HOG descriptor for people detection can be found at
         opencv_source_code/samples/gpu/hog.cpp
     -   (Python) An example applying the HOG descriptor for people detection can be found at
         opencv_source_code/samples/python2/peopledetect.py
  */
-struct CV_EXPORTS HOGDescriptor
+class CV_EXPORTS HOG : public cv::Algorithm
 {
-    enum { DEFAULT_WIN_SIGMA = -1 };
-    enum { DEFAULT_NLEVELS = 64 };
-    enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };
+public:
+    enum
+    {
+        DESCR_FORMAT_ROW_BY_ROW,
+        DESCR_FORMAT_COL_BY_COL
+    };
 
     /** @brief Creates the HOG descriptor and detector.
 
@@ -99,132 +91,105 @@ struct CV_EXPORTS HOGDescriptor
     @param block_stride Block stride. It must be a multiple of cell size.
     @param cell_size Cell size. Only (8, 8) is supported for now.
     @param nbins Number of bins. Only 9 bins per cell are supported for now.
-    @param win_sigma Gaussian smoothing window parameter.
-    @param threshold_L2hys L2-Hys normalization method shrinkage.
-    @param gamma_correction Flag to specify whether the gamma correction preprocessing is required or
-    not.
-    @param nlevels Maximum number of detection window increases.
      */
-    HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16),
-                  Size block_stride=Size(8, 8), Size cell_size=Size(8, 8),
-                  int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA,
-                  double threshold_L2hys=0.2, bool gamma_correction=true,
-                  int nlevels=DEFAULT_NLEVELS);
+    static Ptr<HOG> create(Size win_size = Size(64, 128),
+                           Size block_size = Size(16, 16),
+                           Size block_stride = Size(8, 8),
+                           Size cell_size = Size(8, 8),
+                           int nbins = 9);
+
+    //! Gaussian smoothing window parameter.
+    virtual void setWinSigma(double win_sigma) = 0;
+    virtual double getWinSigma() const = 0;
+
+    //! L2-Hys normalization method shrinkage.
+    virtual void setL2HysThreshold(double threshold_L2hys) = 0;
+    virtual double getL2HysThreshold() const = 0;
+
+    //! Flag to specify whether the gamma correction preprocessing is required or not.
+    virtual void setGammaCorrection(bool gamma_correction) = 0;
+    virtual bool getGammaCorrection() const = 0;
+
+    //! Maximum number of detection window increases.
+    virtual void setNumLevels(int nlevels) = 0;
+    virtual int getNumLevels() const = 0;
+
+    //! Threshold for the distance between features and SVM classifying plane.
+    //! Usually it is 0 and should be specfied in the detector coefficients (as the last free
+    //! coefficient). But if the free coefficient is omitted (which is allowed), you can specify it
+    //! manually here.
+    virtual void setHitThreshold(double hit_threshold) = 0;
+    virtual double getHitThreshold() const = 0;
+
+    //! Window stride. It must be a multiple of block stride.
+    virtual void setWinStride(Size win_stride) = 0;
+    virtual Size getWinStride() const = 0;
+
+    //! Coefficient of the detection window increase.
+    virtual void setScaleFactor(double scale0) = 0;
+    virtual double getScaleFactor() const = 0;
+
+    //! Coefficient to regulate the similarity threshold. When detected, some
+    //! objects can be covered by many rectangles. 0 means not to perform grouping.
+    //! See groupRectangles.
+    virtual void setGroupThreshold(int group_threshold) = 0;
+    virtual int getGroupThreshold() const = 0;
+
+    //! Descriptor storage format:
+    //!   - **DESCR_FORMAT_ROW_BY_ROW** - Row-major order.
+    //!   - **DESCR_FORMAT_COL_BY_COL** - Column-major order.
+    virtual void setDescriptorFormat(int descr_format) = 0;
+    virtual int getDescriptorFormat() const = 0;
 
     /** @brief Returns the number of coefficients required for the classification.
      */
-    size_t getDescriptorSize() const;
+    virtual size_t getDescriptorSize() const = 0;
+
     /** @brief Returns the block histogram size.
-    */
-    size_t getBlockHistogramSize() const;
+     */
+    virtual size_t getBlockHistogramSize() const = 0;
 
     /** @brief Sets coefficients for the linear SVM classifier.
-    */
-    void setSVMDetector(const std::vector<float>& detector);
+     */
+    virtual void setSVMDetector(InputArray detector) = 0;
 
-    /** @brief Returns coefficients of the classifier trained for people detection (for default window size).
-    */
-    static std::vector<float> getDefaultPeopleDetector();
-    /** @brief Returns coefficients of the classifier trained for people detection (for 48x96 windows).
-    */
-    static std::vector<float> getPeopleDetector48x96();
-    /** @brief Returns coefficients of the classifier trained for people detection (for 64x128 windows).
-    */
-    static std::vector<float> getPeopleDetector64x128();
+    /** @brief Returns coefficients of the classifier trained for people detection.
+     */
+    virtual Mat getDefaultPeopleDetector() const = 0;
 
     /** @brief Performs object detection without a multi-scale window.
 
     @param img Source image. CV_8UC1 and CV_8UC4 types are supported for now.
     @param found_locations Left-top corner points of detected objects boundaries.
-    @param hit_threshold Threshold for the distance between features and SVM classifying plane.
-    Usually it is 0 and should be specfied in the detector coefficients (as the last free
-    coefficient). But if the free coefficient is omitted (which is allowed), you can specify it
-    manually here.
-    @param win_stride Window stride. It must be a multiple of block stride.
-    @param padding Mock parameter to keep the CPU interface compatibility. It must be (0,0).
+    @param confidences Optional output array for confidences.
      */
-    void detect(const GpuMat& img, std::vector<Point>& found_locations,
-                double hit_threshold=0, Size win_stride=Size(),
-                Size padding=Size());
+    virtual void detect(InputArray img,
+                        std::vector<Point>& found_locations,
+                        std::vector<double>* confidences = NULL) = 0;
 
     /** @brief Performs object detection with a multi-scale window.
 
     @param img Source image. See cuda::HOGDescriptor::detect for type limitations.
     @param found_locations Detected objects boundaries.
+    @param confidences Optional output array for confidences.
     @param hit_threshold Threshold for the distance between features and SVM classifying plane. See
     cuda::HOGDescriptor::detect for details.
     @param win_stride Window stride. It must be a multiple of block stride.
     @param padding Mock parameter to keep the CPU interface compatibility. It must be (0,0).
-    @param scale0 Coefficient of the detection window increase.
-    @param group_threshold Coefficient to regulate the similarity threshold. When detected, some
-    objects can be covered by many rectangles. 0 means not to perform grouping. See groupRectangles .
      */
-    void detectMultiScale(const GpuMat& img, std::vector<Rect>& found_locations,
-                          double hit_threshold=0, Size win_stride=Size(),
-                          Size padding=Size(), double scale0=1.05,
-                          int group_threshold=2);
-
-    void computeConfidence(const GpuMat& img, std::vector<Point>& hits, double hit_threshold,
-                                                Size win_stride, Size padding, std::vector<Point>& locations, std::vector<double>& confidences);
-
-    void computeConfidenceMultiScale(const GpuMat& img, std::vector<Rect>& found_locations,
-                                                                    double hit_threshold, Size win_stride, Size padding,
-                                                                    std::vector<HOGConfidence> &conf_out, int group_threshold);
+    virtual void detectMultiScale(InputArray img,
+                                  std::vector<Rect>& found_locations,
+                                  std::vector<double>* confidences = NULL) = 0;
 
     /** @brief Returns block descriptors computed for the whole image.
 
     @param img Source image. See cuda::HOGDescriptor::detect for type limitations.
-    @param win_stride Window stride. It must be a multiple of block stride.
     @param descriptors 2D array of descriptors.
-    @param descr_format Descriptor storage format:
-    -   **DESCR_FORMAT_ROW_BY_ROW** - Row-major order.
-    -   **DESCR_FORMAT_COL_BY_COL** - Column-major order.
-
-    The function is mainly used to learn the classifier.
+    @param stream CUDA stream.
      */
-    void getDescriptors(const GpuMat& img, Size win_stride,
-                        GpuMat& descriptors,
-                        int descr_format=DESCR_FORMAT_COL_BY_COL);
-
-    Size win_size;
-    Size block_size;
-    Size block_stride;
-    Size cell_size;
-    int nbins;
-    double win_sigma;
-    double threshold_L2hys;
-    bool gamma_correction;
-    int nlevels;
-
-protected:
-    void computeBlockHistograms(const GpuMat& img);
-    void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle);
-
-    double getWinSigma() const;
-    bool checkDetectorSize() const;
-
-    static int numPartsWithin(int size, int part_size, int stride);
-    static Size numPartsWithin(Size size, Size part_size, Size stride);
-
-    // Coefficients of the separating plane
-    float free_coef;
-    GpuMat detector;
-
-    // Results of the last classification step
-    GpuMat labels, labels_buf;
-    Mat labels_host;
-
-    // Results of the last histogram evaluation step
-    GpuMat block_hists, block_hists_buf;
-
-    // Gradients conputation results
-    GpuMat grad, qangle, grad_buf, qangle_buf;
-
-    // returns subbuffer with required size, reallocates buffer if nessesary.
-    static GpuMat getBuffer(const Size& sz, int type, GpuMat& buf);
-    static GpuMat getBuffer(int rows, int cols, int type, GpuMat& buf);
-
-    std::vector<GpuMat> image_scales;
+    virtual void compute(InputArray img,
+                         OutputArray descriptors,
+                         Stream& stream = Stream::Null()) = 0;
 };
 
 //
diff --git a/modules/cudaobjdetect/perf/perf_objdetect.cpp b/modules/cudaobjdetect/perf/perf_objdetect.cpp
index c5d4649b84..8aeabc4ec0 100644
--- a/modules/cudaobjdetect/perf/perf_objdetect.cpp
+++ b/modules/cudaobjdetect/perf/perf_objdetect.cpp
@@ -71,10 +71,10 @@ PERF_TEST_P(Image, ObjDetect_HOG,
         const cv::cuda::GpuMat d_img(img);
         std::vector<cv::Rect> gpu_found_locations;
 
-        cv::cuda::HOGDescriptor d_hog;
-        d_hog.setSVMDetector(cv::cuda::HOGDescriptor::getDefaultPeopleDetector());
+        cv::Ptr<cv::cuda::HOG> d_hog = cv::cuda::HOG::create();
+        d_hog->setSVMDetector(d_hog->getDefaultPeopleDetector());
 
-        TEST_CYCLE() d_hog.detectMultiScale(d_img, gpu_found_locations);
+        TEST_CYCLE() d_hog->detectMultiScale(d_img, gpu_found_locations);
 
         SANITY_CHECK(gpu_found_locations);
     }
@@ -82,8 +82,10 @@ PERF_TEST_P(Image, ObjDetect_HOG,
     {
         std::vector<cv::Rect> cpu_found_locations;
 
+        cv::Ptr<cv::cuda::HOG> d_hog = cv::cuda::HOG::create();
+
         cv::HOGDescriptor hog;
-        hog.setSVMDetector(cv::cuda::HOGDescriptor::getDefaultPeopleDetector());
+        hog.setSVMDetector(d_hog->getDefaultPeopleDetector());
 
         TEST_CYCLE() hog.detectMultiScale(img, cpu_found_locations);
 
diff --git a/modules/cudaobjdetect/src/hog.cpp b/modules/cudaobjdetect/src/hog.cpp
index a10afa0ae0..1d465ff25c 100644
--- a/modules/cudaobjdetect/src/hog.cpp
+++ b/modules/cudaobjdetect/src/hog.cpp
@@ -42,23 +42,12 @@
 
 #include "precomp.hpp"
 
+using namespace cv;
+using namespace cv::cuda;
+
 #if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
 
-cv::cuda::HOGDescriptor::HOGDescriptor(Size, Size, Size, Size, int, double, double, bool, int) { throw_no_cuda(); }
-size_t cv::cuda::HOGDescriptor::getDescriptorSize() const { throw_no_cuda(); return 0; }
-size_t cv::cuda::HOGDescriptor::getBlockHistogramSize() const { throw_no_cuda(); return 0; }
-double cv::cuda::HOGDescriptor::getWinSigma() const { throw_no_cuda(); return 0; }
-bool cv::cuda::HOGDescriptor::checkDetectorSize() const { throw_no_cuda(); return false; }
-void cv::cuda::HOGDescriptor::setSVMDetector(const std::vector<float>&) { throw_no_cuda(); }
-void cv::cuda::HOGDescriptor::detect(const GpuMat&, std::vector<Point>&, double, Size, Size) { throw_no_cuda(); }
-void cv::cuda::HOGDescriptor::detectMultiScale(const GpuMat&, std::vector<Rect>&, double, Size, Size, double, int) { throw_no_cuda(); }
-void cv::cuda::HOGDescriptor::computeBlockHistograms(const GpuMat&) { throw_no_cuda(); }
-void cv::cuda::HOGDescriptor::getDescriptors(const GpuMat&, Size, GpuMat&, int) { throw_no_cuda(); }
-std::vector<float> cv::cuda::HOGDescriptor::getDefaultPeopleDetector() { throw_no_cuda(); return std::vector<float>(); }
-std::vector<float> cv::cuda::HOGDescriptor::getPeopleDetector48x96() { throw_no_cuda(); return std::vector<float>(); }
-std::vector<float> cv::cuda::HOGDescriptor::getPeopleDetector64x128() { throw_no_cuda(); return std::vector<float>(); }
-void cv::cuda::HOGDescriptor::computeConfidence(const GpuMat&, std::vector<Point>&, double, Size, Size, std::vector<Point>&, std::vector<double>&) { throw_no_cuda(); }
-void cv::cuda::HOGDescriptor::computeConfidenceMultiScale(const GpuMat&, std::vector<Rect>&, double, Size, Size, std::vector<HOGConfidence>&, int) { throw_no_cuda(); }
+Ptr<cuda::HOG> cv::cuda::HOG::create(Size, Size, Size, Size, int) { throw_no_cuda(); return Ptr<cuda::HOG>(); }
 
 #else
 
@@ -102,1518 +91,1607 @@ namespace cv { namespace cuda { namespace device
     }
 }}}
 
-using namespace ::cv::cuda::device;
-
-cv::cuda::HOGDescriptor::HOGDescriptor(Size win_size_, Size block_size_, Size block_stride_, Size cell_size_,
-                                      int nbins_, double win_sigma_, double threshold_L2hys_, bool gamma_correction_, int nlevels_)
-        : win_size(win_size_),
-          block_size(block_size_),
-          block_stride(block_stride_),
-          cell_size(cell_size_),
-          nbins(nbins_),
-          win_sigma(win_sigma_),
-          threshold_L2hys(threshold_L2hys_),
-          gamma_correction(gamma_correction_),
-          nlevels(nlevels_)
-{
-    CV_Assert((win_size.width  - block_size.width ) % block_stride.width  == 0 &&
-              (win_size.height - block_size.height) % block_stride.height == 0);
-
-    CV_Assert(block_size.width % cell_size.width == 0 && block_size.height % cell_size.height == 0);
-
-    CV_Assert(block_stride == cell_size);
-
-    CV_Assert(cell_size == Size(8, 8));
-
-    Size cells_per_block = Size(block_size.width / cell_size.width, block_size.height / cell_size.height);
-    CV_Assert(cells_per_block == Size(2, 2));
-}
-
-size_t cv::cuda::HOGDescriptor::getDescriptorSize() const
-{
-    return numPartsWithin(win_size, block_size, block_stride).area() * getBlockHistogramSize();
-}
+using namespace cv::cuda::device;
 
-size_t cv::cuda::HOGDescriptor::getBlockHistogramSize() const
+namespace
 {
-    Size cells_per_block = Size(block_size.width / cell_size.width, block_size.height / cell_size.height);
-    return (size_t)(nbins * cells_per_block.area());
-}
+    class HOG_Impl : public cv::cuda::HOG
+    {
+    public:
+        HOG_Impl(Size win_size,
+                 Size block_size,
+                 Size block_stride,
+                 Size cell_size,
+                 int nbins);
+
+        virtual void setWinSigma(double win_sigma) { win_sigma_ = win_sigma; }
+        virtual double getWinSigma() const;
+
+        virtual void setL2HysThreshold(double threshold_L2hys) { threshold_L2hys_ = threshold_L2hys; }
+        virtual double getL2HysThreshold() const { return threshold_L2hys_; }
+
+        virtual void setGammaCorrection(bool gamma_correction) { gamma_correction_ = gamma_correction; }
+        virtual bool getGammaCorrection() const { return gamma_correction_; }
+
+        virtual void setNumLevels(int nlevels) { nlevels_ = nlevels; }
+        virtual int getNumLevels() const { return nlevels_; }
+
+        virtual void setHitThreshold(double hit_threshold) { hit_threshold_ = hit_threshold; }
+        virtual double getHitThreshold() const { return hit_threshold_; }
+
+        virtual void setWinStride(Size win_stride) { win_stride_ = win_stride; }
+        virtual Size getWinStride() const { return win_stride_; }
+
+        virtual void setScaleFactor(double scale0) { scale0_ = scale0; }
+        virtual double getScaleFactor() const { return scale0_; }
+
+        virtual void setGroupThreshold(int group_threshold) { group_threshold_ = group_threshold; }
+        virtual int getGroupThreshold() const { return group_threshold_; }
+
+        virtual void setDescriptorFormat(int descr_format) { descr_format_ = descr_format; }
+        virtual int getDescriptorFormat() const { return descr_format_; }
+
+        virtual size_t getDescriptorSize() const;
+
+        virtual size_t getBlockHistogramSize() const;
+
+        virtual void setSVMDetector(InputArray detector);
+
+        virtual Mat getDefaultPeopleDetector() const;
+
+        virtual void detect(InputArray img,
+                            std::vector<Point>& found_locations,
+                            std::vector<double>* confidences);
+
+        virtual void detectMultiScale(InputArray img,
+                                      std::vector<Rect>& found_locations,
+                                      std::vector<double>* confidences);
+
+        virtual void compute(InputArray img,
+                             OutputArray descriptors,
+                             Stream& stream);
+
+    private:
+        Size win_size_;
+        Size block_size_;
+        Size block_stride_;
+        Size cell_size_;
+        int nbins_;
+
+        double win_sigma_;
+        double threshold_L2hys_;
+        bool gamma_correction_;
+        int nlevels_;
+        double hit_threshold_;
+        Size win_stride_;
+        double scale0_;
+        int group_threshold_;
+        int descr_format_;
+
+    private:
+        int getTotalHistSize(Size img_size) const;
+        void computeBlockHistograms(const GpuMat& img, GpuMat& block_hists);
+        void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle);
+
+        // Coefficients of the separating plane
+        float free_coef_;
+        GpuMat detector_;
+    };
+
+    HOG_Impl::HOG_Impl(Size win_size,
+                       Size block_size,
+                       Size block_stride,
+                       Size cell_size,
+                       int nbins) :
+        win_size_(win_size),
+        block_size_(block_size),
+        block_stride_(block_stride),
+        cell_size_(cell_size),
+        nbins_(nbins),
+
+        win_sigma_(-1.0),
+        threshold_L2hys_(0.2),
+        gamma_correction_(true),
+        nlevels_(64),
+        hit_threshold_(0.0),
+        win_stride_(block_stride),
+        scale0_(1.05),
+        group_threshold_(2),
+        descr_format_(DESCR_FORMAT_COL_BY_COL)
+    {
+        CV_Assert((win_size.width  - block_size.width ) % block_stride.width  == 0 &&
+                  (win_size.height - block_size.height) % block_stride.height == 0);
 
-double cv::cuda::HOGDescriptor::getWinSigma() const
-{
-    return win_sigma >= 0 ? win_sigma : (block_size.width + block_size.height) / 8.0;
-}
+        CV_Assert(block_size.width % cell_size.width == 0 &&
+                  block_size.height % cell_size.height == 0);
 
-bool cv::cuda::HOGDescriptor::checkDetectorSize() const
-{
-    size_t detector_size = detector.rows * detector.cols;
-    size_t descriptor_size = getDescriptorSize();
-    return detector_size == 0 || detector_size == descriptor_size || detector_size == descriptor_size + 1;
-}
+        CV_Assert(block_stride == cell_size);
 
-void cv::cuda::HOGDescriptor::setSVMDetector(const std::vector<float>& _detector)
-{
-    std::vector<float> detector_reordered(_detector.size());
+        CV_Assert(cell_size == Size(8, 8));
 
-    size_t block_hist_size = getBlockHistogramSize();
-    cv::Size blocks_per_img = numPartsWithin(win_size, block_size, block_stride);
+        Size cells_per_block(block_size.width / cell_size.width, block_size.height / cell_size.height);
+        CV_Assert(cells_per_block == Size(2, 2));
+    }
 
-    for (int i = 0; i < blocks_per_img.height; ++i)
-        for (int j = 0; j < blocks_per_img.width; ++j)
-        {
-            const float* src = &_detector[0] + (j * blocks_per_img.height + i) * block_hist_size;
-            float* dst = &detector_reordered[0] + (i * blocks_per_img.width + j) * block_hist_size;
-            for (size_t k = 0; k < block_hist_size; ++k)
-                dst[k] = src[k];
-        }
+    static int numPartsWithin(int size, int part_size, int stride)
+    {
+        return (size - part_size + stride) / stride;
+    }
 
-    this->detector.upload(Mat(detector_reordered).reshape(1, 1));
+    static Size numPartsWithin(Size size, Size part_size, Size stride)
+    {
+        return Size(numPartsWithin(size.width, part_size.width, stride.width),
+                    numPartsWithin(size.height, part_size.height, stride.height));
+    }
 
-    size_t descriptor_size = getDescriptorSize();
-    free_coef = _detector.size() > descriptor_size ? _detector[descriptor_size] : 0;
+    size_t HOG_Impl::getDescriptorSize() const
+    {
+        return numPartsWithin(win_size_, block_size_, block_stride_).area() * getBlockHistogramSize();
+    }
 
-    CV_Assert(checkDetectorSize());
-}
+    size_t HOG_Impl::getBlockHistogramSize() const
+    {
+        Size cells_per_block(block_size_.width / cell_size_.width, block_size_.height / cell_size_.height);
+        return nbins_ * cells_per_block.area();
+    }
 
-cv::cuda::GpuMat cv::cuda::HOGDescriptor::getBuffer(const Size& sz, int type, GpuMat& buf)
-{
-    if (buf.empty() || buf.type() != type)
-        buf.create(sz, type);
-    else
-        if (buf.cols < sz.width || buf.rows < sz.height)
-            buf.create(std::max(buf.rows, sz.height), std::max(buf.cols, sz.width), type);
+    double HOG_Impl::getWinSigma() const
+    {
+        return win_sigma_ >= 0 ? win_sigma_ : (block_size_.width + block_size_.height) / 8.0;
+    }
 
-    return buf(Rect(Point(0,0), sz));
-}
+    void HOG_Impl::setSVMDetector(InputArray _detector)
+    {
+        const int descriptor_size = static_cast<int>(getDescriptorSize());
 
-cv::cuda::GpuMat cv::cuda::HOGDescriptor::getBuffer(int rows, int cols, int type, GpuMat& buf)
-{
-    return getBuffer(Size(cols, rows), type, buf);
-}
+        const Mat detector = _detector.getMat();
 
+        CV_Assert( detector.type() == CV_32FC1 );
+        CV_Assert( detector.rows == 1 );
+        CV_Assert( detector.cols == descriptor_size || detector.cols == descriptor_size + 1 );
 
-void cv::cuda::HOGDescriptor::computeGradient(const GpuMat& img, GpuMat& _grad, GpuMat& _qangle)
-{
-    CV_Assert(img.type() == CV_8UC1 || img.type() == CV_8UC4);
+        std::vector<float> detector_reordered(detector.ptr<float>(), detector.ptr<float>() + detector.cols);
 
-    //   grad.create(img.size(), CV_32FC2);
-    _grad = getBuffer(img.size(), CV_32FC2, grad_buf);
+        size_t block_hist_size = getBlockHistogramSize();
+        Size blocks_per_win = numPartsWithin(win_size_, block_size_, block_stride_);
 
-    //   qangle.create(img.size(), CV_8UC2);
-    _qangle = getBuffer(img.size(), CV_8UC2, qangle_buf);
+        for (int i = 0; i < blocks_per_win.height; ++i)
+        {
+            for (int j = 0; j < blocks_per_win.width; ++j)
+            {
+                const float* src = detector.ptr<float>() + (j * blocks_per_win.height + i) * block_hist_size;
+                float* dst = &detector_reordered[0] + (i * blocks_per_win.width + j) * block_hist_size;
+                for (size_t k = 0; k < block_hist_size; ++k)
+                    dst[k] = src[k];
+            }
+        }
 
-    float angleScale = (float)(nbins / CV_PI);
-    switch (img.type())
-    {
-        case CV_8UC1:
-            hog::compute_gradients_8UC1(nbins, img.rows, img.cols, img, angleScale, _grad, _qangle, gamma_correction);
-            break;
-        case CV_8UC4:
-            hog::compute_gradients_8UC4(nbins, img.rows, img.cols, img, angleScale, _grad, _qangle, gamma_correction);
-            break;
+        detector_.upload(Mat(detector_reordered).reshape(1, 1));
+        free_coef_ = detector.cols > descriptor_size ? detector.at<float>(0, descriptor_size) : 0;
     }
-}
-
-
-void cv::cuda::HOGDescriptor::computeBlockHistograms(const GpuMat& img)
-{
-    cv::Size blocks_per_win = numPartsWithin(win_size, block_size, block_stride);
-    hog::set_up_constants(nbins, block_stride.width, block_stride.height, blocks_per_win.width, blocks_per_win.height);
-
-    computeGradient(img, grad, qangle);
 
-    size_t block_hist_size = getBlockHistogramSize();
-    Size blocks_per_img = numPartsWithin(img.size(), block_size, block_stride);
+    static Mat getPeopleDetector64x128();
+    static Mat getPeopleDetector48x96();
 
-    //   block_hists.create(1, block_hist_size * blocks_per_img.area(), CV_32F);
-    block_hists = getBuffer(1, static_cast<int>(block_hist_size * blocks_per_img.area()), CV_32F, block_hists_buf);
+    Mat HOG_Impl::getDefaultPeopleDetector() const
+    {
+        CV_Assert( win_size_ == Size(64, 128) || win_size_ == Size(48, 96) );
 
-    hog::compute_hists(nbins, block_stride.width, block_stride.height, img.rows, img.cols,
-                        grad, qangle, (float)getWinSigma(), block_hists.ptr<float>());
+        if (win_size_ == Size(64, 128))
+            return getPeopleDetector64x128();
+        else
+            return getPeopleDetector48x96();
+    }
 
-    hog::normalize_hists(nbins, block_stride.width, block_stride.height, img.rows, img.cols,
-                         block_hists.ptr<float>(), (float)threshold_L2hys);
-}
+    void HOG_Impl::detect(InputArray _img, std::vector<Point>& hits, std::vector<double>* confidences)
+    {
+        const GpuMat img = _img.getGpuMat();
 
+        CV_Assert( img.type() == CV_8UC1 || img.type() == CV_8UC4 );
+        CV_Assert( win_stride_.width % block_stride_.width == 0 && win_stride_.height % block_stride_.height == 0 );
 
-void cv::cuda::HOGDescriptor::getDescriptors(const GpuMat& img, Size win_stride, GpuMat& descriptors, int descr_format)
-{
-    CV_Assert(win_stride.width % block_stride.width == 0 && win_stride.height % block_stride.height == 0);
+        hits.clear();
+        if (detector_.empty())
+            return;
 
-    computeBlockHistograms(img);
+        BufferPool pool(Stream::Null());
 
-    const size_t block_hist_size = getBlockHistogramSize();
-    Size blocks_per_win = numPartsWithin(win_size, block_size, block_stride);
-    Size wins_per_img   = numPartsWithin(img.size(), win_size, win_stride);
+        GpuMat block_hists = pool.getBuffer(1, getTotalHistSize(img.size()), CV_32FC1);
+        computeBlockHistograms(img, block_hists);
 
-    descriptors.create(wins_per_img.area(), static_cast<int>(blocks_per_win.area() * block_hist_size), CV_32F);
+        Size wins_per_img = numPartsWithin(img.size(), win_size_, win_stride_);
 
-    switch (descr_format)
-    {
-    case DESCR_FORMAT_ROW_BY_ROW:
-        hog::extract_descrs_by_rows(win_size.height, win_size.width, block_stride.height, block_stride.width,
-                                    win_stride.height, win_stride.width, img.rows, img.cols, block_hists.ptr<float>(), descriptors);
-        break;
-    case DESCR_FORMAT_COL_BY_COL:
-        hog::extract_descrs_by_cols(win_size.height, win_size.width, block_stride.height, block_stride.width,
-                                    win_stride.height, win_stride.width, img.rows, img.cols, block_hists.ptr<float>(), descriptors);
-        break;
-    default:
-        CV_Error(cv::Error::StsBadArg, "Unknown descriptor format");
+        if (confidences == NULL)
+        {
+            GpuMat labels = pool.getBuffer(1, wins_per_img.area(), CV_8UC1);
+
+            hog::classify_hists(win_size_.height, win_size_.width,
+                                block_stride_.height, block_stride_.width,
+                                win_stride_.height, win_stride_.width,
+                                img.rows, img.cols,
+                                block_hists.ptr<float>(),
+                                detector_.ptr<float>(),
+                                (float)free_coef_,
+                                (float)hit_threshold_,
+                                labels.ptr());
+
+            Mat labels_host;
+            labels.download(labels_host);
+            unsigned char* vec = labels_host.ptr();
+
+            for (int i = 0; i < wins_per_img.area(); i++)
+            {
+                int y = i / wins_per_img.width;
+                int x = i - wins_per_img.width * y;
+                if (vec[i])
+                    hits.push_back(Point(x * win_stride_.width, y * win_stride_.height));
+            }
+        }
+        else
+        {
+            GpuMat labels = pool.getBuffer(1, wins_per_img.area(), CV_32FC1);
+
+            hog::compute_confidence_hists(win_size_.height, win_size_.width,
+                                          block_stride_.height, block_stride_.width,
+                                          win_stride_.height, win_stride_.width,
+                                          img.rows, img.cols,
+                                          block_hists.ptr<float>(),
+                                          detector_.ptr<float>(),
+                                          (float)free_coef_,
+                                          (float)hit_threshold_,
+                                          labels.ptr<float>());
+
+            Mat labels_host;
+            labels.download(labels_host);
+            float* vec = labels_host.ptr<float>();
+
+            confidences->clear();
+            for (int i = 0; i < wins_per_img.area(); i++)
+            {
+                int y = i / wins_per_img.width;
+                int x = i - wins_per_img.width * y;
+
+                if (vec[i] >= hit_threshold_)
+                {
+                    hits.push_back(Point(x * win_stride_.width, y * win_stride_.height));
+                    confidences->push_back((double)vec[i]);
+                }
+            }
+        }
     }
-}
-
-void cv::cuda::HOGDescriptor::computeConfidence(const GpuMat& img, std::vector<Point>& hits, double hit_threshold,
-                          Size win_stride, Size padding, std::vector<Point>& locations, std::vector<double>& confidences)
-{
-  CV_Assert(padding == Size(0, 0));
 
-  hits.clear();
-  if (detector.empty())
-    return;
+    void HOG_Impl::detectMultiScale(InputArray _img,
+                                    std::vector<Rect>& found_locations,
+                                    std::vector<double>* confidences)
+    {
+        const GpuMat img = _img.getGpuMat();
 
-  computeBlockHistograms(img);
+        CV_Assert( img.type() == CV_8UC1 || img.type() == CV_8UC4 );
+        CV_Assert( confidences == NULL || group_threshold_ == 0 );
 
-  if (win_stride == Size())
-    win_stride = block_stride;
-  else
-    CV_Assert(win_stride.width % block_stride.width == 0 &&
-         win_stride.height % block_stride.height == 0);
+        std::vector<double> level_scale;
+        double scale = 1.0;
+        int levels = 0;
+        for (levels = 0; levels < nlevels_; levels++)
+        {
+            level_scale.push_back(scale);
 
-  Size wins_per_img = numPartsWithin(img.size(), win_size, win_stride);
-  labels.create(1, wins_per_img.area(), CV_32F);
+            if (cvRound(img.cols / scale) < win_size_.width ||
+                cvRound(img.rows / scale) < win_size_.height ||
+                scale0_ <= 1)
+            {
+                break;
+            }
 
-  hog::compute_confidence_hists(win_size.height, win_size.width, block_stride.height, block_stride.width,
-               win_stride.height, win_stride.width, img.rows, img.cols, block_hists.ptr<float>(),
-               detector.ptr<float>(), (float)free_coef, (float)hit_threshold, labels.ptr<float>());
+            scale *= scale0_;
+        }
+        levels = std::max(levels, 1);
+        level_scale.resize(levels);
 
-  labels.download(labels_host);
-  float* vec = labels_host.ptr<float>();
+        std::vector<Point> level_hits;
+        std::vector<double> level_confidences;
 
-  // does not support roi for now..
-  locations.clear();
-  confidences.clear();
-  for (int i = 0; i < wins_per_img.area(); i++)
-    {
-      int y = i / wins_per_img.width;
-      int x = i - wins_per_img.width * y;
-      if (vec[i] >= hit_threshold)
-   hits.push_back(Point(x * win_stride.width, y * win_stride.height));
-
-      Point pt(win_stride.width * x, win_stride.height * y);
-      locations.push_back(pt);
-      confidences.push_back((double)vec[i]);
-    }
-}
+        BufferPool pool(Stream::Null());
 
-void cv::cuda::HOGDescriptor::computeConfidenceMultiScale(const GpuMat& img, std::vector<Rect>& found_locations,
-                            double hit_threshold, Size win_stride, Size padding,
-                            std::vector<HOGConfidence> &conf_out, int group_threshold)
-{
-    std::vector<double> level_scale;
-    double scale = 1.;
-    int levels = 0;
+        found_locations.clear();
+        for (size_t i = 0; i < level_scale.size(); i++)
+        {
+            scale = level_scale[i];
 
-    for (levels = 0; levels < (int)conf_out.size(); levels++)
-    {
-        scale = conf_out[levels].scale;
-        level_scale.push_back(scale);
-        if (cvRound(img.cols/scale) < win_size.width || cvRound(img.rows/scale) < win_size.height)
-            break;
-    }
+            Size sz(cvRound(img.cols / scale), cvRound(img.rows / scale));
 
-    levels = std::max(levels, 1);
-    level_scale.resize(levels);
+            GpuMat smaller_img;
+            if (sz == img.size())
+            {
+                smaller_img = img;
+            }
+            else
+            {
+                smaller_img = pool.getBuffer(sz, img.type());
+                switch (img.type())
+                {
+                    case CV_8UC1: hog::resize_8UC1(img, smaller_img); break;
+                    case CV_8UC4: hog::resize_8UC4(img, smaller_img); break;
+                }
+            }
 
-    std::vector<Rect> all_candidates;
-    std::vector<Point> locations;
+            detect(smaller_img, level_hits,
+                   confidences ? &level_confidences : NULL);
 
-    for (size_t i = 0; i < level_scale.size(); i++)
-    {
-        scale = level_scale[i];
-        Size sz(cvRound(img.cols / scale), cvRound(img.rows / scale));
-        GpuMat smaller_img;
+            Size scaled_win_size(cvRound(win_size_.width * scale),
+                                 cvRound(win_size_.height * scale));
 
-        if (sz == img.size())
-            smaller_img = img;
-        else
-        {
-            smaller_img.create(sz, img.type());
-            switch (img.type())
+            for (size_t j = 0; j < level_hits.size(); j++)
             {
-            case CV_8UC1: hog::resize_8UC1(img, smaller_img); break;
-            case CV_8UC4: hog::resize_8UC4(img, smaller_img); break;
+                found_locations.push_back(Rect(Point2d(level_hits[j]) * scale, scaled_win_size));
+                if (confidences)
+                    confidences->push_back(level_confidences[j]);
             }
         }
 
-        computeConfidence(smaller_img, locations, hit_threshold, win_stride, padding, conf_out[i].locations, conf_out[i].confidences);
-
-        Size scaled_win_size(cvRound(win_size.width * scale), cvRound(win_size.height * scale));
-        for (size_t j = 0; j < locations.size(); j++)
-            all_candidates.push_back(Rect(Point2d(locations[j]) * scale, scaled_win_size));
+        if (group_threshold_ > 0)
+        {
+            groupRectangles(found_locations, group_threshold_, 0.2/*magic number copied from CPU version*/);
+        }
     }
 
-    found_locations.assign(all_candidates.begin(), all_candidates.end());
-    groupRectangles(found_locations, group_threshold, 0.2/*magic number copied from CPU version*/);
-}
-
+    void HOG_Impl::compute(InputArray _img,
+                           OutputArray _descriptors,
+                           Stream& stream)
+    {
+        const GpuMat img = _img.getGpuMat();
 
-void cv::cuda::HOGDescriptor::detect(const GpuMat& img, std::vector<Point>& hits, double hit_threshold, Size win_stride, Size padding)
-{
-    CV_Assert(img.type() == CV_8UC1 || img.type() == CV_8UC4);
-    CV_Assert(padding == Size(0, 0));
+        CV_Assert( img.type() == CV_8UC1 || img.type() == CV_8UC4 );
+        CV_Assert( win_stride_.width % block_stride_.width == 0 && win_stride_.height % block_stride_.height == 0 );
+        CV_Assert( !stream );
 
-    hits.clear();
-    if (detector.empty())
-        return;
+        BufferPool pool(stream);
 
-    computeBlockHistograms(img);
+        GpuMat block_hists = pool.getBuffer(1, getTotalHistSize(img.size()), CV_32FC1);
+        computeBlockHistograms(img, block_hists);
 
-    if (win_stride == Size())
-        win_stride = block_stride;
-    else
-        CV_Assert(win_stride.width % block_stride.width == 0 && win_stride.height % block_stride.height == 0);
+        const size_t block_hist_size = getBlockHistogramSize();
+        Size blocks_per_win = numPartsWithin(win_size_, block_size_, block_stride_);
+        Size wins_per_img   = numPartsWithin(img.size(), win_size_, win_stride_);
 
-    Size wins_per_img = numPartsWithin(img.size(), win_size, win_stride);
-    //   labels.create(1, wins_per_img.area(), CV_8U);
-    labels = getBuffer(1, wins_per_img.area(), CV_8U, labels_buf);
+        _descriptors.create(wins_per_img.area(), static_cast<int>(blocks_per_win.area() * block_hist_size), CV_32FC1);
+        GpuMat descriptors = _descriptors.getGpuMat();
 
-    hog::classify_hists(win_size.height, win_size.width, block_stride.height, block_stride.width,
-                        win_stride.height, win_stride.width, img.rows, img.cols, block_hists.ptr<float>(),
-                        detector.ptr<float>(), (float)free_coef, (float)hit_threshold, labels.ptr());
+        switch (descr_format_)
+        {
+        case DESCR_FORMAT_ROW_BY_ROW:
+            hog::extract_descrs_by_rows(win_size_.height, win_size_.width,
+                                        block_stride_.height, block_stride_.width,
+                                        win_stride_.height, win_stride_.width,
+                                        img.rows, img.cols,
+                                        block_hists.ptr<float>(),
+                                        descriptors);
+            break;
+        case DESCR_FORMAT_COL_BY_COL:
+            hog::extract_descrs_by_cols(win_size_.height, win_size_.width,
+                                        block_stride_.height, block_stride_.width,
+                                        win_stride_.height, win_stride_.width,
+                                        img.rows, img.cols,
+                                        block_hists.ptr<float>(),
+                                        descriptors);
+            break;
+        default:
+            CV_Error(cv::Error::StsBadArg, "Unknown descriptor format");
+        }
+    }
 
-    labels.download(labels_host);
-    unsigned char* vec = labels_host.ptr();
-    for (int i = 0; i < wins_per_img.area(); i++)
+    int HOG_Impl::getTotalHistSize(Size img_size) const
     {
-        int y = i / wins_per_img.width;
-        int x = i - wins_per_img.width * y;
-        if (vec[i])
-            hits.push_back(Point(x * win_stride.width, y * win_stride.height));
+        size_t block_hist_size = getBlockHistogramSize();
+        Size blocks_per_img = numPartsWithin(img_size, block_size_, block_stride_);
+        return static_cast<int>(block_hist_size * blocks_per_img.area());
     }
-}
 
+    void HOG_Impl::computeBlockHistograms(const GpuMat& img, GpuMat& block_hists)
+    {
+        cv::Size blocks_per_win = numPartsWithin(win_size_, block_size_, block_stride_);
+        hog::set_up_constants(nbins_, block_stride_.width, block_stride_.height, blocks_per_win.width, blocks_per_win.height);
 
+        BufferPool pool(Stream::Null());
 
-void cv::cuda::HOGDescriptor::detectMultiScale(const GpuMat& img, std::vector<Rect>& found_locations, double hit_threshold,
-                                              Size win_stride, Size padding, double scale0, int group_threshold)
-{
+        GpuMat grad = pool.getBuffer(img.size(), CV_32FC2);
+        GpuMat qangle = pool.getBuffer(img.size(), CV_8UC2);
+        computeGradient(img, grad, qangle);
 
-    CV_Assert(img.type() == CV_8UC1 || img.type() == CV_8UC4);
+        block_hists.create(1, getTotalHistSize(img.size()), CV_32FC1);
 
-    std::vector<double> level_scale;
-    double scale = 1.;
-    int levels = 0;
+        hog::compute_hists(nbins_,
+                           block_stride_.width, block_stride_.height,
+                           img.rows, img.cols,
+                           grad, qangle,
+                           (float)getWinSigma(),
+                           block_hists.ptr<float>());
 
-    for (levels = 0; levels < nlevels; levels++)
-    {
-        level_scale.push_back(scale);
-        if (cvRound(img.cols/scale) < win_size.width ||
-            cvRound(img.rows/scale) < win_size.height || scale0 <= 1)
-            break;
-        scale *= scale0;
+        hog::normalize_hists(nbins_,
+                             block_stride_.width, block_stride_.height,
+                             img.rows, img.cols,
+                             block_hists.ptr<float>(),
+                             (float)threshold_L2hys_);
     }
-    levels = std::max(levels, 1);
-    level_scale.resize(levels);
-    image_scales.resize(levels);
 
-    std::vector<Rect> all_candidates;
-    std::vector<Point> locations;
-
-    for (size_t i = 0; i < level_scale.size(); i++)
+    void HOG_Impl::computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle)
     {
-        scale = level_scale[i];
-        Size sz(cvRound(img.cols / scale), cvRound(img.rows / scale));
-        GpuMat smaller_img;
+        grad.create(img.size(), CV_32FC2);
+        qangle.create(img.size(), CV_8UC2);
 
-        if (sz == img.size())
-            smaller_img = img;
-        else
+        float angleScale = (float)(nbins_ / CV_PI);
+        switch (img.type())
         {
-            image_scales[i].create(sz, img.type());
-            switch (img.type())
-            {
-                case CV_8UC1: hog::resize_8UC1(img, image_scales[i]); break;
-                case CV_8UC4: hog::resize_8UC4(img, image_scales[i]); break;
-            }
-            smaller_img = image_scales[i];
+            case CV_8UC1:
+                hog::compute_gradients_8UC1(nbins_, img.rows, img.cols, img, angleScale, grad, qangle, gamma_correction_);
+                break;
+            case CV_8UC4:
+                hog::compute_gradients_8UC4(nbins_, img.rows, img.cols, img, angleScale, grad, qangle, gamma_correction_);
+                break;
         }
-
-        detect(smaller_img, locations, hit_threshold, win_stride, padding);
-        Size scaled_win_size(cvRound(win_size.width * scale), cvRound(win_size.height * scale));
-        for (size_t j = 0; j < locations.size(); j++)
-            all_candidates.push_back(Rect(Point2d(locations[j]) * scale, scaled_win_size));
     }
-
-    found_locations.assign(all_candidates.begin(), all_candidates.end());
-    groupRectangles(found_locations, group_threshold, 0.2/*magic number copied from CPU version*/);
 }
 
-int cv::cuda::HOGDescriptor::numPartsWithin(int size, int part_size, int stride)
+Ptr<cuda::HOG> cv::cuda::HOG::create(Size win_size,
+                                     Size block_size,
+                                     Size block_stride,
+                                     Size cell_size,
+                                     int nbins)
 {
-    return (size - part_size + stride) / stride;
+    return makePtr<HOG_Impl>(win_size, block_size, block_stride, cell_size, nbins);
 }
 
-cv::Size cv::cuda::HOGDescriptor::numPartsWithin(cv::Size size, cv::Size part_size, cv::Size stride)
+namespace
 {
-    return Size(numPartsWithin(size.width, part_size.width, stride.width), numPartsWithin(size.height, part_size.height, stride.height));
-}
-
-std::vector<float> cv::cuda::HOGDescriptor::getDefaultPeopleDetector()
-{
-    return getPeopleDetector64x128();
-}
-
-std::vector<float> cv::cuda::HOGDescriptor::getPeopleDetector48x96()
-{
-    static const float detector[] = {
-        0.294350f, -0.098796f, -0.129522f, 0.078753f, 0.387527f, 0.261529f,
-        0.145939f, 0.061520f, 0.328699f, 0.227148f, -0.066467f, -0.086723f,
-        0.047559f, 0.106714f, 0.037897f, 0.111461f, -0.024406f, 0.304769f,
-        0.254676f, -0.069235f, 0.082566f, 0.147260f, 0.326969f, 0.148888f,
-        0.055270f, -0.087985f, 0.261720f, 0.143442f, 0.026812f, 0.238212f,
-        0.194020f, 0.056341f, -0.025854f, -0.034444f, -0.156631f, 0.205174f,
-        0.089008f, -0.139811f, -0.100147f, -0.037830f, -0.029230f, -0.055641f,
-        0.033248f, -0.016512f, 0.155244f, 0.247315f, -0.124694f, -0.048414f,
-        -0.062219f, 0.193683f, 0.004574f, 0.055089f, 0.093565f, 0.167712f,
-        0.167581f, 0.018895f, 0.215258f, 0.122609f, 0.090520f, -0.067219f,
-        -0.049029f, -0.099615f, 0.241804f, -0.094893f, -0.176248f, 0.001727f,
-        -0.134473f, 0.104442f, 0.050942f, 0.081165f, 0.072156f, 0.121646f,
-        0.002656f, -0.297974f, -0.133587f, -0.060121f, -0.092515f, -0.048974f,
-        -0.084754f, -0.180111f, -0.038590f, 0.086283f, -0.134636f, -0.107249f,
-        0.132890f, 0.141556f, 0.249425f, 0.130273f, -0.030031f, 0.073212f,
-        -0.008155f, 0.019931f, 0.071688f, 0.000300f, -0.019525f, -0.021725f,
-        -0.040993f, -0.086841f, 0.070124f, 0.240033f, 0.265350f, 0.043208f,
-        0.166754f, 0.091453f, 0.060916f, -0.036972f, -0.091043f, 0.079873f,
-        0.219781f, 0.158102f, -0.140618f, -0.043016f, 0.124802f, 0.093668f,
-        0.103208f, 0.094872f, 0.080541f, 0.137711f, 0.160566f, -0.169231f,
-        0.013983f, 0.309508f, -0.004217f, -0.057200f, -0.064489f, 0.014066f,
-        0.361009f, 0.251328f, -0.080983f, -0.044183f, 0.061436f, -0.037381f,
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-        0.366159f, -0.179938f, -0.148508f, 0.006043f, 0.170048f, 0.097866f,
-        -0.102658f, -0.260430f, 0.248868f, 0.037019f, -0.118111f, 0.078176f,
-        0.194171f, 0.211328f, 0.368612f, 0.361213f, 0.130013f, 0.094650f,
-        0.227396f, -0.178058f, -0.114782f, -0.008093f, 0.231080f, -0.011843f,
-        -0.097917f, -0.325788f, 0.141879f, 0.119738f, -0.230427f, -0.117419f,
-        -0.114153f, 0.037903f, 0.116383f, 0.218773f, -0.101884f, 0.059466f,
-        0.119255f, 0.010874f, -0.031449f, 0.045996f, 0.119931f, 0.273760f,
-        0.311700f, 0.261794f, 0.194809f, 0.339829f, 0.239449f, 0.064140f,
-        0.077597f, 0.098996f, 0.143534f, 0.184602f, 0.037507f, 0.225494f,
-        0.096142f, -0.147370f, -0.207833f, -0.174742f, -0.086391f, -0.038942f,
-        0.159577f, -0.088492f, -0.000989f, 0.108154f, -0.025890f, -0.072713f,
-        0.025997f, -0.006803f, -0.086879f, -0.011290f, -0.269200f, -0.103450f,
-        -0.124910f, -0.116340f, 0.141459f, 0.208800f, 0.042268f, 0.265034f,
-        0.516474f, 0.217591f, -0.018843f, -0.313328f, -0.168363f, 0.047129f,
-        0.090480f, -0.109852f, -0.018761f, 0.210669f, 0.281269f, -0.043591f,
-        -0.034147f, -0.237772f, -0.134843f, -0.072481f, -0.103831f, 0.038355f,
-        0.308619f, 0.148023f, -0.045867f, -0.123950f, -0.210860f, -0.064973f,
-        -0.036308f, -0.046731f, -0.022099f, 0.095776f, 0.409423f, 0.060635f,
-        -0.065196f, 0.051828f, 0.027981f, -0.009609f, -0.137681f, -0.095011f,
-        -0.019045f, 0.177278f, 0.009759f, -0.092119f, -0.016958f, -0.133860f,
-        -0.118421f, -0.032039f, -0.006214f, -0.084541f, 0.063971f, -0.073642f,
-        0.165676f, 0.110443f, 0.044131f, 0.046568f, 0.053292f, -0.055466f,
-        0.015512f, 0.371947f, 0.232102f, -0.016923f, 0.103979f, -0.091758f,
-        0.005907f, 0.209100f, 0.157433f, 0.030518f, 0.250366f, 0.062322f,
-        0.036720f, 0.094676f, 0.017306f, -0.010328f, -0.079012f, 0.016781f,
-        -0.112435f, 0.061795f, 0.042543f, -0.126799f, -0.009975f, -0.056760f,
-        0.046424f, -0.194712f, -0.139399f, -0.037731f, 0.157989f, -0.016261f,
-        0.123345f, 0.230563f, 0.083300f, -0.016392f, 0.059567f, -0.016035f,
-        -0.064767f, 0.231945f, 0.156629f, 0.034602f, 0.145628f, 0.041315f,
-        0.034535f, 0.019967f, -0.089188f, -0.012091f, 0.307857f, 0.211405f,
-        -0.025091f, -0.148249f, -0.129384f, 0.063536f, -0.068603f, -0.067941f,
-        -0.035104f, 0.210832f, 0.063810f, 0.062764f, -0.089889f, -0.030554f,
-        0.014791f, -0.053362f, -0.037818f, -0.196640f, 0.008388f, -0.082654f,
-        0.143056f, 0.064221f, 0.069795f, 0.191040f, 0.097321f, -0.028679f,
-        0.075794f, 0.313154f, 0.086240f, 0.207643f, 0.017809f, 0.122867f,
-        0.224586f, 0.167403f, -0.023884f, 0.047434f, 0.344091f, 0.187745f,
-        0.136177f, 0.141738f, 0.063799f, 0.045233f, -0.077342f, -0.003525f,
-        -0.165041f, -0.025616f, -0.073745f, 0.164439f, 0.011200f, -0.145896f,
-        -0.027954f, -0.061987f, -0.039874f, -0.142775f, 0.151042f, -0.038238f,
-        0.053152f, 0.078615f, 0.086061f, 0.100593f, 0.128046f, -0.071006f,
-        -0.116558f, 0.208445f, 0.051086f, 0.076843f, 0.023191f, -0.084781f,
-        -0.011790f, 0.147807f, -0.048554f, -0.113932f, 0.283322f, 0.190934f,
-        0.092789f, 0.033018f, -0.142428f, -0.142480f, -0.099023f, -0.041020f,
-        -0.042760f, 0.203295f, -0.053475f, 0.042424f, 0.222839f, -0.019167f,
-        -0.133176f, -0.276216f, -0.031998f, 0.117290f, 0.177827f, -0.059973f,
-        -0.064744f, -0.117040f, -0.155482f, -0.099531f, 0.164121f, -0.026682f,
-        -0.093810f, 0.238993f, -0.006506f, 0.007830f, 0.065819f, -0.203643f,
-        -0.100925f, -0.053652f, -0.130770f, 0.026277f, 0.131796f, 0.032742f,
-        0.127186f, 0.116694f, -0.161122f, -0.279773f, -0.252515f, -0.002638f,
-        0.042812f, 0.096776f, -0.123280f, 0.064858f, -0.010455f, -0.219760f,
-        -0.239331f, -0.104363f, -0.058022f, -0.053584f, 0.025611f, 0.005129f,
-        -0.100418f, -0.045712f, -0.194418f, -0.126366f, -0.030530f, 0.051168f,
-        0.215959f, 0.172402f, -0.054700f, -0.185995f, -0.278360f, -0.193693f,
-        -0.040309f, 0.003735f, -0.007770f, 0.123556f, 0.190179f, -0.077315f,
-        0.117403f, 0.212942f, 0.012160f, 0.000113f, 0.027331f, 0.040202f,
-        0.033293f, 0.219438f, 0.184174f, 0.259349f, 0.311206f, 0.082547f,
-        -0.047875f, -0.078417f, 0.010746f, 0.082620f, 0.311931f, 0.307605f,
-        0.003863f, 0.021405f, -0.026388f, -0.019572f, 0.020582f, -0.059353f,
-        0.025199f, 0.261319f, 0.086316f, 0.143614f, 0.107780f, 0.003900f,
-        -0.188397f, -0.038563f, -0.106045f, -0.125154f, -0.010509f, 0.054021f,
-        0.242130f, 0.279152f, 0.215546f, 0.346995f, 0.440856f, 0.237452f,
-        0.234154f, 0.301646f, 0.168929f, -0.208358f, -0.126848f, 0.010260f,
-        0.121018f, -0.062975f, -0.052848f, 0.050341f, -0.061103f, -0.266482f,
-        0.107186f, 0.140221f, 0.280065f, 0.287889f, 0.373198f, 0.151596f,
-        0.013593f, 0.115616f, 0.014616f, -0.281710f, -0.237597f, -0.117305f,
-        -0.000034f, -0.136739f, -0.196275f, -0.095225f, -0.125310f, -0.250514f,
-        0.236804f, -0.071805f, -0.037421f, 0.048230f, 0.321596f, 0.063632f,
-        0.024039f, -0.029133f, 0.230983f, 0.160593f, -0.154355f, -0.013086f,
-        -0.079929f, 0.094692f, 0.160391f, 0.180239f, 0.053895f, 0.100759f,
-        0.288631f, 0.038191f, 0.181692f, 0.229682f, 0.440166f, 0.063401f,
-        0.006273f, 0.020865f, 0.338695f, 0.256244f, -0.043927f, 0.115617f,
-        0.003296f, 0.173965f, 0.021318f, -0.040936f, -0.118932f, 0.182380f,
-        0.235922f, -0.053233f, -0.015053f, -0.101057f, 0.095341f, 0.051111f,
-        0.161831f, 0.032614f, 0.159496f, 0.072375f, 0.025089f, 0.023748f,
-        0.029151f, 0.161284f, -0.117717f, -0.036191f, -0.176822f, -0.162006f,
-        0.226542f, -0.078329f, 0.043079f, -0.119172f, 0.054614f, -0.101365f,
-        -0.064541f, -0.115304f, 0.135170f, 0.298872f, 0.098060f, 0.089428f,
-        -0.007497f, 0.110391f, -0.028824f, 0.020835f, -0.036804f, 0.125411f,
-        0.192105f, -0.048931f, 0.003086f, -0.010681f, 0.074698f, -0.016263f,
-        0.096063f, 0.060267f, -0.007277f, 0.139139f, -0.080635f, 0.036628f,
-        0.086058f, 0.131979f, 0.085707f, 0.025301f, 0.226094f, 0.194759f,
-        0.042193f, -0.157846f, -0.068402f, -0.141450f, -0.112659f, -0.076305f,
-        -0.069085f, -0.114332f, -0.102005f, 0.132193f, -0.067042f, 0.106643f,
-        0.198964f, 0.171616f, 0.167237f, -0.033730f, -0.026755f, 0.083621f,
-        0.149459f, -0.002799f, -0.000318f, 0.011753f, 0.065889f, -0.089375f,
-        -0.049610f, 0.224579f, 0.216548f, -0.034908f, -0.017851f, -0.088144f,
-        0.007530f, 0.240268f, 0.073270f, 0.013263f, 0.175323f, 0.012082f,
-        0.093993f, 0.015282f, 0.105854f, 0.107990f, 0.077798f, -0.096166f,
-        -0.079607f, 0.177820f, 0.142392f, 0.033337f, -0.078100f, -0.081616f,
-        -0.046993f, 0.139459f, 0.020272f, -0.123161f, 0.175269f, 0.105217f,
-        0.057328f, 0.080909f, -0.012612f, -0.097081f, 0.082060f, -0.096716f,
-        -0.063921f, 0.201884f, 0.128166f, -0.035051f, -0.032227f, -0.068139f,
-        -0.115915f, 0.095080f, -0.086007f, -0.067543f, 0.030776f, 0.032712f,
-        0.088937f, 0.054336f, -0.039329f, -0.114022f, 0.171672f, -0.112321f,
-        -0.217646f, 0.065186f, 0.060223f, 0.192174f, 0.055580f, -0.131107f,
-        -0.144338f, 0.056730f, -0.034707f, -0.081616f, -0.135298f, -0.000614f,
-        0.087189f, 0.014614f, 0.067709f, 0.107689f, 0.225780f, 0.084361f,
-        -0.008544f, 0.051649f, -0.048369f, -0.037739f, -0.060710f, 0.002654f,
-        0.016935f, 0.085563f, -0.015961f, -0.019265f, 0.111788f, 0.062376f,
-        0.202019f, 0.047713f, 0.042261f, 0.069716f, 0.242913f, 0.021052f,
-        -0.072812f, -0.155920f, -0.026436f, 0.035621f, -0.079300f, -0.028787f,
-        -0.048329f, 0.084718f, -0.060565f, -0.083750f, -0.164075f, -0.040742f,
-        -0.086219f, 0.015271f, -0.005204f, -0.016038f, 0.045816f, -0.050433f,
-        -0.077652f, 0.117109f, 0.009611f, -0.009045f, -0.008634f, -0.055373f,
-        -0.085968f, 0.028527f, -0.054736f, -0.168089f, 0.175839f, 0.071205f,
-        -0.023603f, 0.037907f, -0.004561f, -0.022634f, 0.123831f, 0.094469f,
-        -0.072920f, -0.133642f, -0.014032f, -0.142754f, -0.026999f, -0.199409f,
-        0.013268f, 0.226989f, 0.048650f, -0.170988f, -0.050141f, 0.007880f,
-        0.061880f, 0.019078f, -0.043578f, -0.038139f, 0.134814f, 0.054097f,
-        -0.081670f, 0.176838f, 0.047920f, -0.038176f, 0.050406f, -0.107181f,
-        -0.036279f, 0.027060f, 0.081594f, -0.002820f, 0.090507f, -0.033338f,
-        -0.059571f, 0.013404f, -0.099860f, 0.073371f, 0.342805f, 0.098305f,
-        -0.150910f, -0.020822f, -0.056960f, 0.046262f, -0.043413f, -0.149405f,
-        -0.129105f, -0.010899f, -0.014229f, -0.179949f, -0.113044f, -0.049468f,
-        -0.065513f, 0.090269f, -0.011919f, 0.087846f, 0.095796f, 0.146127f,
-        0.101599f, 0.078066f, -0.084348f, -0.100002f, -0.020134f, -0.050169f,
-        0.062122f, 0.014640f, 0.019143f, 0.036543f, 0.180924f, -0.013976f,
-        -0.066768f, -0.001090f, -0.070419f, -0.004839f, -0.001504f, 0.034483f,
-        -0.044954f, -0.050336f, -0.088638f, -0.174782f, -0.116082f, -0.205507f,
-        0.015587f, -0.042839f, -0.096879f, -0.144097f, -0.050268f, -0.196796f,
-        0.109639f, 0.271411f, 0.173732f, 0.108070f, 0.156437f, 0.124255f,
-        0.097242f, 0.238693f, 0.083941f, 0.109105f, 0.223940f, 0.267188f,
-        0.027385f, 0.025819f, 0.125070f, 0.093738f, 0.040353f, 0.038645f,
-        -0.012730f, 0.144063f, 0.052931f, -0.009138f, 0.084193f, 0.160272f,
-        -0.041366f, 0.011951f, -0.121446f, -0.106713f, -0.047566f, 0.047984f,
-        -0.255224f, -0.076116f, 0.098685f, -0.150845f, -0.171513f, -0.156590f,
-        0.058331f, 0.187493f, 0.413018f, 0.554265f, 0.372242f, 0.237943f,
-        0.124571f, 0.110829f, 0.010322f, -0.174477f, -0.067627f, -0.001979f,
-        0.142913f, 0.040597f, 0.019907f, 0.025963f, -0.043585f, -0.120732f,
-        0.099937f, 0.091059f, 0.247307f, 0.204226f, -0.042753f, -0.068580f,
-        -0.119002f, 0.026722f, 0.034853f, -0.060934f, -0.025054f, -0.093026f,
-        -0.035372f, -0.233209f, -0.049869f, -0.039151f, -0.022279f, -0.065380f,
-        -9.063785f };
-    return std::vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
-}
-
-
-
+    static Mat getPeopleDetector48x96()
+    {
+        static float detector[] = {
+            0.294350f, -0.098796f, -0.129522f, 0.078753f, 0.387527f, 0.261529f,
+            0.145939f, 0.061520f, 0.328699f, 0.227148f, -0.066467f, -0.086723f,
+            0.047559f, 0.106714f, 0.037897f, 0.111461f, -0.024406f, 0.304769f,
+            0.254676f, -0.069235f, 0.082566f, 0.147260f, 0.326969f, 0.148888f,
+            0.055270f, -0.087985f, 0.261720f, 0.143442f, 0.026812f, 0.238212f,
+            0.194020f, 0.056341f, -0.025854f, -0.034444f, -0.156631f, 0.205174f,
+            0.089008f, -0.139811f, -0.100147f, -0.037830f, -0.029230f, -0.055641f,
+            0.033248f, -0.016512f, 0.155244f, 0.247315f, -0.124694f, -0.048414f,
+            -0.062219f, 0.193683f, 0.004574f, 0.055089f, 0.093565f, 0.167712f,
+            0.167581f, 0.018895f, 0.215258f, 0.122609f, 0.090520f, -0.067219f,
+            -0.049029f, -0.099615f, 0.241804f, -0.094893f, -0.176248f, 0.001727f,
+            -0.134473f, 0.104442f, 0.050942f, 0.081165f, 0.072156f, 0.121646f,
+            0.002656f, -0.297974f, -0.133587f, -0.060121f, -0.092515f, -0.048974f,
+            -0.084754f, -0.180111f, -0.038590f, 0.086283f, -0.134636f, -0.107249f,
+            0.132890f, 0.141556f, 0.249425f, 0.130273f, -0.030031f, 0.073212f,
+            -0.008155f, 0.019931f, 0.071688f, 0.000300f, -0.019525f, -0.021725f,
+            -0.040993f, -0.086841f, 0.070124f, 0.240033f, 0.265350f, 0.043208f,
+            0.166754f, 0.091453f, 0.060916f, -0.036972f, -0.091043f, 0.079873f,
+            0.219781f, 0.158102f, -0.140618f, -0.043016f, 0.124802f, 0.093668f,
+            0.103208f, 0.094872f, 0.080541f, 0.137711f, 0.160566f, -0.169231f,
+            0.013983f, 0.309508f, -0.004217f, -0.057200f, -0.064489f, 0.014066f,
+            0.361009f, 0.251328f, -0.080983f, -0.044183f, 0.061436f, -0.037381f,
+            -0.078786f, 0.030993f, 0.066314f, 0.037683f, 0.152325f, -0.091683f,
+            0.070203f, 0.217856f, 0.036435f, -0.076462f, 0.006254f, -0.094431f,
+            0.154829f, -0.023038f, -0.196961f, -0.024594f, 0.178465f, -0.050139f,
+            -0.045932f, -0.000965f, 0.109112f, 0.046165f, -0.159373f, -0.008713f,
+            0.041307f, 0.097129f, -0.057211f, -0.064599f, 0.077165f, 0.176167f,
+            0.138322f, 0.065753f, -0.104950f, 0.017933f, 0.136255f, -0.011598f,
+            0.047007f, 0.080550f, 0.068619f, 0.084661f, -0.035493f, -0.091314f,
+            -0.041411f, 0.060971f, -0.101912f, -0.079870f, -0.085977f, -0.022686f,
+            0.079788f, -0.098064f, -0.054603f, 0.040383f, 0.300794f, 0.128603f,
+            0.094844f, 0.047407f, 0.101825f, 0.061832f, -0.162160f, -0.204553f,
+            -0.035165f, 0.101450f, -0.016641f, -0.027140f, -0.134392f, -0.008743f,
+            0.102331f, 0.114853f, 0.009644f, 0.062823f, 0.237339f, 0.167843f,
+            0.053066f, -0.012592f, 0.043158f, 0.002305f, 0.065001f, -0.038929f,
+            -0.020356f, 0.152343f, 0.043469f, -0.029967f, -0.042948f, 0.032481f,
+            0.068488f, -0.110840f, -0.111083f, 0.111980f, -0.002072f, -0.005562f,
+            0.082926f, 0.006635f, -0.108153f, 0.024242f, -0.086464f, -0.189884f,
+            -0.017492f, 0.191456f, -0.007683f, -0.128769f, -0.038017f, -0.132380f,
+            0.091926f, 0.079696f, -0.106728f, -0.007656f, 0.172744f, 0.011576f,
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+            -0.118421f, -0.032039f, -0.006214f, -0.084541f, 0.063971f, -0.073642f,
+            0.165676f, 0.110443f, 0.044131f, 0.046568f, 0.053292f, -0.055466f,
+            0.015512f, 0.371947f, 0.232102f, -0.016923f, 0.103979f, -0.091758f,
+            0.005907f, 0.209100f, 0.157433f, 0.030518f, 0.250366f, 0.062322f,
+            0.036720f, 0.094676f, 0.017306f, -0.010328f, -0.079012f, 0.016781f,
+            -0.112435f, 0.061795f, 0.042543f, -0.126799f, -0.009975f, -0.056760f,
+            0.046424f, -0.194712f, -0.139399f, -0.037731f, 0.157989f, -0.016261f,
+            0.123345f, 0.230563f, 0.083300f, -0.016392f, 0.059567f, -0.016035f,
+            -0.064767f, 0.231945f, 0.156629f, 0.034602f, 0.145628f, 0.041315f,
+            0.034535f, 0.019967f, -0.089188f, -0.012091f, 0.307857f, 0.211405f,
+            -0.025091f, -0.148249f, -0.129384f, 0.063536f, -0.068603f, -0.067941f,
+            -0.035104f, 0.210832f, 0.063810f, 0.062764f, -0.089889f, -0.030554f,
+            0.014791f, -0.053362f, -0.037818f, -0.196640f, 0.008388f, -0.082654f,
+            0.143056f, 0.064221f, 0.069795f, 0.191040f, 0.097321f, -0.028679f,
+            0.075794f, 0.313154f, 0.086240f, 0.207643f, 0.017809f, 0.122867f,
+            0.224586f, 0.167403f, -0.023884f, 0.047434f, 0.344091f, 0.187745f,
+            0.136177f, 0.141738f, 0.063799f, 0.045233f, -0.077342f, -0.003525f,
+            -0.165041f, -0.025616f, -0.073745f, 0.164439f, 0.011200f, -0.145896f,
+            -0.027954f, -0.061987f, -0.039874f, -0.142775f, 0.151042f, -0.038238f,
+            0.053152f, 0.078615f, 0.086061f, 0.100593f, 0.128046f, -0.071006f,
+            -0.116558f, 0.208445f, 0.051086f, 0.076843f, 0.023191f, -0.084781f,
+            -0.011790f, 0.147807f, -0.048554f, -0.113932f, 0.283322f, 0.190934f,
+            0.092789f, 0.033018f, -0.142428f, -0.142480f, -0.099023f, -0.041020f,
+            -0.042760f, 0.203295f, -0.053475f, 0.042424f, 0.222839f, -0.019167f,
+            -0.133176f, -0.276216f, -0.031998f, 0.117290f, 0.177827f, -0.059973f,
+            -0.064744f, -0.117040f, -0.155482f, -0.099531f, 0.164121f, -0.026682f,
+            -0.093810f, 0.238993f, -0.006506f, 0.007830f, 0.065819f, -0.203643f,
+            -0.100925f, -0.053652f, -0.130770f, 0.026277f, 0.131796f, 0.032742f,
+            0.127186f, 0.116694f, -0.161122f, -0.279773f, -0.252515f, -0.002638f,
+            0.042812f, 0.096776f, -0.123280f, 0.064858f, -0.010455f, -0.219760f,
+            -0.239331f, -0.104363f, -0.058022f, -0.053584f, 0.025611f, 0.005129f,
+            -0.100418f, -0.045712f, -0.194418f, -0.126366f, -0.030530f, 0.051168f,
+            0.215959f, 0.172402f, -0.054700f, -0.185995f, -0.278360f, -0.193693f,
+            -0.040309f, 0.003735f, -0.007770f, 0.123556f, 0.190179f, -0.077315f,
+            0.117403f, 0.212942f, 0.012160f, 0.000113f, 0.027331f, 0.040202f,
+            0.033293f, 0.219438f, 0.184174f, 0.259349f, 0.311206f, 0.082547f,
+            -0.047875f, -0.078417f, 0.010746f, 0.082620f, 0.311931f, 0.307605f,
+            0.003863f, 0.021405f, -0.026388f, -0.019572f, 0.020582f, -0.059353f,
+            0.025199f, 0.261319f, 0.086316f, 0.143614f, 0.107780f, 0.003900f,
+            -0.188397f, -0.038563f, -0.106045f, -0.125154f, -0.010509f, 0.054021f,
+            0.242130f, 0.279152f, 0.215546f, 0.346995f, 0.440856f, 0.237452f,
+            0.234154f, 0.301646f, 0.168929f, -0.208358f, -0.126848f, 0.010260f,
+            0.121018f, -0.062975f, -0.052848f, 0.050341f, -0.061103f, -0.266482f,
+            0.107186f, 0.140221f, 0.280065f, 0.287889f, 0.373198f, 0.151596f,
+            0.013593f, 0.115616f, 0.014616f, -0.281710f, -0.237597f, -0.117305f,
+            -0.000034f, -0.136739f, -0.196275f, -0.095225f, -0.125310f, -0.250514f,
+            0.236804f, -0.071805f, -0.037421f, 0.048230f, 0.321596f, 0.063632f,
+            0.024039f, -0.029133f, 0.230983f, 0.160593f, -0.154355f, -0.013086f,
+            -0.079929f, 0.094692f, 0.160391f, 0.180239f, 0.053895f, 0.100759f,
+            0.288631f, 0.038191f, 0.181692f, 0.229682f, 0.440166f, 0.063401f,
+            0.006273f, 0.020865f, 0.338695f, 0.256244f, -0.043927f, 0.115617f,
+            0.003296f, 0.173965f, 0.021318f, -0.040936f, -0.118932f, 0.182380f,
+            0.235922f, -0.053233f, -0.015053f, -0.101057f, 0.095341f, 0.051111f,
+            0.161831f, 0.032614f, 0.159496f, 0.072375f, 0.025089f, 0.023748f,
+            0.029151f, 0.161284f, -0.117717f, -0.036191f, -0.176822f, -0.162006f,
+            0.226542f, -0.078329f, 0.043079f, -0.119172f, 0.054614f, -0.101365f,
+            -0.064541f, -0.115304f, 0.135170f, 0.298872f, 0.098060f, 0.089428f,
+            -0.007497f, 0.110391f, -0.028824f, 0.020835f, -0.036804f, 0.125411f,
+            0.192105f, -0.048931f, 0.003086f, -0.010681f, 0.074698f, -0.016263f,
+            0.096063f, 0.060267f, -0.007277f, 0.139139f, -0.080635f, 0.036628f,
+            0.086058f, 0.131979f, 0.085707f, 0.025301f, 0.226094f, 0.194759f,
+            0.042193f, -0.157846f, -0.068402f, -0.141450f, -0.112659f, -0.076305f,
+            -0.069085f, -0.114332f, -0.102005f, 0.132193f, -0.067042f, 0.106643f,
+            0.198964f, 0.171616f, 0.167237f, -0.033730f, -0.026755f, 0.083621f,
+            0.149459f, -0.002799f, -0.000318f, 0.011753f, 0.065889f, -0.089375f,
+            -0.049610f, 0.224579f, 0.216548f, -0.034908f, -0.017851f, -0.088144f,
+            0.007530f, 0.240268f, 0.073270f, 0.013263f, 0.175323f, 0.012082f,
+            0.093993f, 0.015282f, 0.105854f, 0.107990f, 0.077798f, -0.096166f,
+            -0.079607f, 0.177820f, 0.142392f, 0.033337f, -0.078100f, -0.081616f,
+            -0.046993f, 0.139459f, 0.020272f, -0.123161f, 0.175269f, 0.105217f,
+            0.057328f, 0.080909f, -0.012612f, -0.097081f, 0.082060f, -0.096716f,
+            -0.063921f, 0.201884f, 0.128166f, -0.035051f, -0.032227f, -0.068139f,
+            -0.115915f, 0.095080f, -0.086007f, -0.067543f, 0.030776f, 0.032712f,
+            0.088937f, 0.054336f, -0.039329f, -0.114022f, 0.171672f, -0.112321f,
+            -0.217646f, 0.065186f, 0.060223f, 0.192174f, 0.055580f, -0.131107f,
+            -0.144338f, 0.056730f, -0.034707f, -0.081616f, -0.135298f, -0.000614f,
+            0.087189f, 0.014614f, 0.067709f, 0.107689f, 0.225780f, 0.084361f,
+            -0.008544f, 0.051649f, -0.048369f, -0.037739f, -0.060710f, 0.002654f,
+            0.016935f, 0.085563f, -0.015961f, -0.019265f, 0.111788f, 0.062376f,
+            0.202019f, 0.047713f, 0.042261f, 0.069716f, 0.242913f, 0.021052f,
+            -0.072812f, -0.155920f, -0.026436f, 0.035621f, -0.079300f, -0.028787f,
+            -0.048329f, 0.084718f, -0.060565f, -0.083750f, -0.164075f, -0.040742f,
+            -0.086219f, 0.015271f, -0.005204f, -0.016038f, 0.045816f, -0.050433f,
+            -0.077652f, 0.117109f, 0.009611f, -0.009045f, -0.008634f, -0.055373f,
+            -0.085968f, 0.028527f, -0.054736f, -0.168089f, 0.175839f, 0.071205f,
+            -0.023603f, 0.037907f, -0.004561f, -0.022634f, 0.123831f, 0.094469f,
+            -0.072920f, -0.133642f, -0.014032f, -0.142754f, -0.026999f, -0.199409f,
+            0.013268f, 0.226989f, 0.048650f, -0.170988f, -0.050141f, 0.007880f,
+            0.061880f, 0.019078f, -0.043578f, -0.038139f, 0.134814f, 0.054097f,
+            -0.081670f, 0.176838f, 0.047920f, -0.038176f, 0.050406f, -0.107181f,
+            -0.036279f, 0.027060f, 0.081594f, -0.002820f, 0.090507f, -0.033338f,
+            -0.059571f, 0.013404f, -0.099860f, 0.073371f, 0.342805f, 0.098305f,
+            -0.150910f, -0.020822f, -0.056960f, 0.046262f, -0.043413f, -0.149405f,
+            -0.129105f, -0.010899f, -0.014229f, -0.179949f, -0.113044f, -0.049468f,
+            -0.065513f, 0.090269f, -0.011919f, 0.087846f, 0.095796f, 0.146127f,
+            0.101599f, 0.078066f, -0.084348f, -0.100002f, -0.020134f, -0.050169f,
+            0.062122f, 0.014640f, 0.019143f, 0.036543f, 0.180924f, -0.013976f,
+            -0.066768f, -0.001090f, -0.070419f, -0.004839f, -0.001504f, 0.034483f,
+            -0.044954f, -0.050336f, -0.088638f, -0.174782f, -0.116082f, -0.205507f,
+            0.015587f, -0.042839f, -0.096879f, -0.144097f, -0.050268f, -0.196796f,
+            0.109639f, 0.271411f, 0.173732f, 0.108070f, 0.156437f, 0.124255f,
+            0.097242f, 0.238693f, 0.083941f, 0.109105f, 0.223940f, 0.267188f,
+            0.027385f, 0.025819f, 0.125070f, 0.093738f, 0.040353f, 0.038645f,
+            -0.012730f, 0.144063f, 0.052931f, -0.009138f, 0.084193f, 0.160272f,
+            -0.041366f, 0.011951f, -0.121446f, -0.106713f, -0.047566f, 0.047984f,
+            -0.255224f, -0.076116f, 0.098685f, -0.150845f, -0.171513f, -0.156590f,
+            0.058331f, 0.187493f, 0.413018f, 0.554265f, 0.372242f, 0.237943f,
+            0.124571f, 0.110829f, 0.010322f, -0.174477f, -0.067627f, -0.001979f,
+            0.142913f, 0.040597f, 0.019907f, 0.025963f, -0.043585f, -0.120732f,
+            0.099937f, 0.091059f, 0.247307f, 0.204226f, -0.042753f, -0.068580f,
+            -0.119002f, 0.026722f, 0.034853f, -0.060934f, -0.025054f, -0.093026f,
+            -0.035372f, -0.233209f, -0.049869f, -0.039151f, -0.022279f, -0.065380f,
+            -9.063785f };
+
+        return Mat(1, static_cast<int>(sizeof(detector)/sizeof(detector[0])), CV_32FC1, detector);
+    }
 
-std::vector<float> cv::cuda::HOGDescriptor::getPeopleDetector64x128()
-{
-    static const float detector[] = {
-       0.05359386f, -0.14721455f, -0.05532170f, 0.05077307f,
-       0.11547081f, -0.04268804f, 0.04635834f, -0.05468199f, 0.08232084f,
-       0.10424068f, -0.02294518f, 0.01108519f, 0.01378693f, 0.11193510f,
-       0.01268418f, 0.08528346f, -0.06309239f, 0.13054633f, 0.08100729f,
-       -0.05209739f, -0.04315529f, 0.09341384f, 0.11035026f, -0.07596218f,
-       -0.05517511f, -0.04465296f, 0.02947334f, 0.04555536f,
-       -3.55954492e-003f, 0.07818956f, 0.07730991f, 0.07890715f, 0.06222893f,
-       0.09001380f, -0.03574381f, 0.03414327f, 0.05677258f, -0.04773581f,
-       0.03746637f, -0.03521175f, 0.06955440f, -0.03849038f, 0.01052293f,
-       0.01736112f, 0.10867710f, 0.08748853f, 3.29739624e-003f, 0.10907028f,
-       0.07913758f, 0.10393070f, 0.02091867f, 0.11594022f, 0.13182420f,
-       0.09879354f, 0.05362710f, -0.06745391f, -7.01260753e-003f,
-       5.24702156e-003f, 0.03236255f, 0.01407916f, 0.02207983f, 0.02537322f,
-       0.04547948f, 0.07200756f, 0.03129894f, -0.06274468f, 0.02107014f,
-       0.06035208f, 0.08636236f, 4.53164103e-003f, 0.02193363f, 0.02309801f,
-       0.05568166f, -0.02645093f, 0.04448695f, 0.02837519f, 0.08975694f,
-       0.04461516f, 0.08975355f, 0.07514391f, 0.02306982f, 0.10410084f,
-       0.06368385f, 0.05943464f, 4.58420580e-003f, 0.05220337f, 0.06675851f,
-       0.08358569f, 0.06712101f, 0.06559004f, -0.03930482f, -9.15936660e-003f,
-       -0.05897915f, 0.02816453f, 0.05032348f, 0.06780671f, 0.03377650f,
-       -6.09417039e-004f, -0.01795146f, -0.03083684f, -0.01302475f,
-       -0.02972313f, 7.88706727e-003f, -0.03525961f, -2.50397739e-003f,
-       0.05245084f, 0.11791293f, -0.02167498f, 0.05299332f, 0.06640524f,
-       0.05190265f, -8.27316567e-003f, 0.03033127f, 0.05842173f,
-       -4.01050318e-003f, -6.25105947e-003f, 0.05862958f, -0.02465461f,
-       0.05546781f, -0.08228195f, -0.07234028f, 0.04640540f, -0.01308254f,
-       -0.02506191f, 0.03100746f, -0.04665651f, -0.04591486f, 0.02949927f,
-       0.06035462f, 0.02244646f, -0.01698639f, 0.01040041f, 0.01131170f,
-       0.05419579f, -0.02130277f, -0.04321722f, -0.03665198f, 0.01126490f,
-       -0.02606488f, -0.02228328f, -0.02255680f, -0.03427236f,
-       -7.75165204e-003f, -0.06195229f, 8.21638294e-003f, 0.09535975f,
-       -0.03709979f, -0.06942501f, 0.14579427f, -0.05448192f, -0.02055904f,
-       0.05747357f, 0.02781788f, -0.07077577f, -0.05178314f, -0.10429011f,
-       -0.11235505f, 0.07529039f, -0.07559302f, -0.08786739f, 0.02983843f,
-       0.02667585f, 0.01382199f, -0.01797496f, -0.03141199f, -0.02098101f,
-       0.09029204f, 0.04955018f, 0.13718739f, 0.11379953f, 1.80019124e-003f,
-       -0.04577610f, -1.11108483e-003f, -0.09470536f, -0.11596080f,
-       0.04489342f, 0.01784211f, 3.06850672e-003f, 0.10781866f,
-       3.36498418e-003f, -0.10842580f, -0.07436839f, -0.10535070f,
-       -0.01866805f, 0.16057891f, -5.07316366e-003f, -0.04295658f,
-       -5.90488780e-003f, 8.82003549e-003f, -0.01492646f, -0.05029279f,
-       -0.12875880f, 8.78831954e-004f, -0.01297184f, -0.07592774f,
-       -0.02668831f, -6.93787413e-004f, 0.02406698f, -0.01773298f,
-       -0.03855745f, -0.05877856f, 0.03259695f, 0.12826584f, 0.06292590f,
-       -4.10733931e-003f, 0.10996531f, 0.01332991f, 0.02088735f, 0.04037504f,
-       -0.05210760f, 0.07760046f, 0.06399347f, -0.05751930f, -0.10053057f,
-       0.07505023f, -0.02139782f, 0.01796176f, 2.34400877e-003f, -0.04208319f,
-       0.07355055f, 0.05093350f, -0.02996780f, -0.02219072f, 0.03355330f,
-       0.04418742f, -0.05580705f, -0.05037573f, -0.04548179f, 0.01379514f,
-       0.02150671f, -0.02194211f, -0.13682702f, 0.05464972f, 0.01608082f,
-       0.05309116f, 0.04701022f, 1.33690401e-003f, 0.07575664f, 0.09625306f,
-       8.92647635e-003f, -0.02819123f, 0.10866830f, -0.03439325f,
-       -0.07092371f, -0.06004780f, -0.02712298f, -7.07467366e-003f,
-       -0.01637020f, 0.01336790f, -0.10313606f, 0.04906582f, -0.05732445f,
-       -0.02731079f, 0.01042235f, -0.08340668f, 0.03686501f, 0.06108340f,
-       0.01322748f, -0.07809529f, 0.03774724f, -0.03413248f, -0.06096525f,
-       -0.04212124f, -0.07982176f, -1.25973229e-003f, -0.03045501f,
-       -0.01236493f, -0.06312395f, 0.04789570f, -0.04602066f, 0.08576570f,
-       0.02521080f, 0.02988098f, 0.10314583f, 0.07060035f, 0.04520544f,
-       -0.04426654f, 0.13146530f, 0.08386490f, 0.02164590f, -2.12280243e-003f,
-       -0.03686353f, -0.02074944f, -0.03829959f, -0.01530596f, 0.02689708f,
-       0.11867401f, -0.06043470f, -0.02785023f, -0.04775074f, 0.04878745f,
-       0.06350956f, 0.03494788f, 0.01467400f, 1.17890188e-003f, 0.04379614f,
-       2.03681854e-003f, -0.03958609f, -0.01072688f, 6.43705716e-003f,
-       0.02996500f, -0.03418507f, -0.01960307f, -0.01219154f,
-       -4.37000440e-003f, -0.02549453f, 0.02646318f, -0.01632513f,
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-       0.03809111f, 0.07265724f, 0.05758125f, -0.03741554f, 0.01648608f,
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-       0.08529541f, 0.13547510f, 0.11189661f, 0.03998901f, 0.09575938f,
-       -0.02631102f, -0.03458253f, -0.04749985f, -0.06070716f,
-       4.71884012e-003f, 0.06445789f, -0.02450038f, -0.05483776f,
-       -0.04657237f, -0.02030717f, -0.03480766f, -0.09397731f, -0.06399718f,
-       -0.01804585f, 5.62348310e-003f, -6.64811488e-003f, -0.06517869f,
-       6.96210237e-003f, -0.01860148f, -0.04245830f, -0.05850367f,
-       -3.24417115e-003f, 0.07700698f, 0.11290991f, 0.09923030f, -0.02970599f,
-       0.05592411f, 0.04813979f, -0.09811195f, -0.09357996f, -0.03276114f,
-       0.05218338f, 0.04141375f, 3.92977800e-003f, -0.05047480f, 0.15960084f,
-       0.04612800f, -0.03114098f, -0.04650044f, -0.03249795f, -0.02425641f,
-       -0.04311355f, 0.04307659f, -0.09401883f, -0.04742785f, -0.01254499f,
-       -0.06598741f, 3.41369561e-003f, -0.05620445f, -7.28127593e-003f,
-       -0.05998361f, -0.03274450f, -0.07376868f, 3.19015374e-003f,
-       -0.07733069f, 0.05815864f, -0.02471071f, 0.03850617f, 0.13838784f,
-       0.15399861f, 0.01731321f, -0.01477586f, 0.10393341f, 0.05159833f,
-       -0.01945555f, -0.03427503f, -0.04867341f, 0.09237480f, 0.10732719f,
-       0.06071450f, -0.01355071f, 0.01844356f, -0.03480803f, -0.03796671f,
-       2.15628621e-004f, -0.05440186f, 0.01889855f, -0.01443413f,
-       -0.02607902f, -0.02938001f, 0.02720689f, -0.06228397f, -0.02970936f,
-       -0.03426210f, -0.10280876f, -0.06739304f, -0.05227850f, 0.03360292f,
-       -0.11278441f, -0.06966180f, -0.13937433f, 9.10932291e-003f,
-       2.52020749e-004f, -4.07359656e-003f, 0.12310639f, 0.09343060f,
-       0.07302511f, 0.03222093f, 0.07532879f, 0.03792387f, -0.04985180f,
-       0.01804602f, 0.02694195f, 0.13481498f, 0.04601225f, 0.04106982f,
-       0.08511057f, 0.12314661f, 0.01320830f, 0.05044121f, -5.52943908e-003f,
-       -0.08992624f, -0.02249301f, -0.08181777f, 0.06165213f, -0.03256603f,
-       -0.01068920f, -0.01323473f, -0.11970232f, -0.04616347f, -0.12088681f,
-       -0.06762606f, -0.08676834f, -0.06434575f, 0.01772529f, 0.03469615f,
-       -0.10926618f, 0.03013873f, 0.14030397f, 0.16130108f, 0.17985588f,
-       0.11281928f, 0.10530639f, 0.08905948f, 0.07733764f, 0.06695238f,
-       0.02142088f, 0.06438877f, 0.09794453f, 0.05745072f, 0.02788557f,
-       0.02632830f, 0.07985807f, 4.24902979e-003f, 8.47890321e-003f,
-       -0.02679466f, -5.28812688e-003f, -0.02162580f, -0.07490715f,
-       -0.08251337f, -0.02056576f, -0.01026194f, -1.15492963e-003f,
-       -5.75720915e-004f, -0.07210591f, -0.07320981f, -0.04883312f,
-       -0.10897151f, -0.07477258f, -0.08867134f, -0.09222437f, -0.10924666f,
-       -0.10430276f, 0.07953499f, 0.02767959f, 0.11393359f, 0.18779543f,
-       0.03313421f, 0.02143700f, 0.05852016f, -2.12067598e-003f,
-       -3.76984011e-003f, 0.02774167f, -0.03124610f, 0.01465141f, 0.01616004f,
-       -0.01391913f, -0.04404102f, -0.05444227f, -0.14684731f, -0.15016587f,
-       0.04509468f, 1.29563001e-003f, 0.01398350f, 0.05610404f, -0.04868806f,
-       -0.04776716f, -8.16873740e-003f, -2.30126386e-003f, -0.02286313f,
-       0.11983398f, -0.04703261f, -0.08814441f, -0.07585249f, -0.10799607f,
-       -0.03232087f, 0.01509786f, -0.04843464f, -0.03967846f, 0.09589416f,
-       0.01352560f, -0.01458119f, 0.01050829f, -0.03038946f, 0.01608388f,
-       1.11975556e-003f, -0.01250656f, 2.86211423e-003f, 0.04333691f,
-       -0.14603497f, -0.01946543f, -0.02327525f, -0.01973944f, 0.07944400f,
-       -0.02224544f, -0.06701808f, 0.03476532f, 0.11505594f, -0.02712801f,
-       -0.01665113f, 0.06315716f, -0.08205860f, 0.07431999f, 0.04915778f,
-       -0.04468752f, -0.01490402f, 0.07400476f, -0.11650901f, 0.05102430f,
-       0.04559118f, -0.05916039f, 0.08840760f, -0.01587902f, -0.14890194f,
-       0.07857784f, 0.04710254f, -0.05381983f, -0.07331945f, -0.03604643f,
-       0.15611970f, 0.07649943f, -0.05959348f, -0.02776607f, 0.11098688f,
-       0.03758875f, -0.04446875f, 0.04933187f, 0.01345535f, 0.06921103f,
-       0.07364785f, 0.05518956f, 0.02899585f, 0.09375840f, 0.10518434f,
-       -0.04420241f, 0.01915282f, -3.56386811e-003f, 0.14586878f, 0.10286101f,
-       -0.04360626f, -0.12723237f, 0.09076386f, 0.11119842f, -0.06035013f,
-       0.09674817f, 0.08938243f, 0.07065924f, 0.02603180f, 5.84815582e-003f,
-       -0.05922065f, 0.12360309f, 3.59695964e-003f, 2.99844006e-003f,
-       0.03697936f, 0.02043072f, 0.04168725f, 0.01025975f, -0.01359980f,
-       -0.01600920f, 0.02581056f, 0.02329250f, 2.98100687e-003f, 0.01629762f,
-       0.06652115f, 0.05855627f, 0.01237463f, -0.01297135f, 0.01761587f,
-       0.05090865f, 0.06549342f, -0.04425945f, 2.43203156e-003f,
-       3.07327788e-003f, 0.06678630f, -0.04303836f, 0.01082393f, -0.06476044f,
-       0.04077786f, 0.12441979f, 0.08237778f, 0.07424165f, 0.04065890f,
-       0.06905543f, 0.09556347f, 0.12724875f, -0.02132082f, 0.08514154f,
-       -0.04175328f, -0.02666954f, 0.01897836f, 0.03317382f, 9.45465732e-003f,
-       -0.01238974f, -0.04242500f, -0.01419479f, -0.03545213f, -0.02440874f,
-       0.08684119f, 0.04212951f, 0.02462858f, -0.01104825f, -5.01706870e-003f,
-       0.02968982f, 0.02597476f, -0.01568939f, 0.04514892f, 0.06974549f,
-       0.08670278f, 0.06828108f, 0.10238872f, 0.05405957f, 0.06548470f,
-       -0.03763957f, 0.01366090f, 0.07069602f, 0.05363748f, 0.04798120f,
-       0.11706422f, 0.05466456f, -0.01869259f, 0.06344382f, 0.03106543f,
-       0.08432506f, -0.02061096f, 0.03821088f, -6.92190882e-003f,
-       6.40467042e-003f, -0.01271779f, 6.89014705e-005f, 0.04541415f,
-       -0.01899539f, -0.05020239f, 0.03000903f, 0.01090422f, 4.52452758e-003f,
-       0.02573632f, -0.02388454f, -0.04200457f, 1.72783900e-003f,
-       -0.05978370f, -0.02720562f, 0.06573715f, 0.01154317f, 0.01265615f,
-       0.07375994f, -9.19828378e-003f, -0.04914120f, 0.02124831f, 0.06455322f,
-       0.04372910f, -0.03310043f, 0.03605788f, -6.78055827e-003f,
-       9.36202332e-003f, 0.01747596f, -0.06406314f, -0.06812935f, 0.08080816f,
-       -0.02778088f, 0.02735260f, 0.06393493f, 0.06652229f, 0.05676993f,
-       0.08640018f, -7.59188086e-003f, -0.02012847f, -0.04741159f,
-       -0.01657069f, -0.01624399f, 0.05547778f, -2.33309763e-003f,
-       0.01120033f, 0.06141156f, -0.06285004f, -0.08732341f, -0.09313398f,
-       -0.04267832f, 5.57443965e-003f, 0.04809862f, 0.01773641f,
-       5.37361018e-003f, 0.14842421f, -0.06298012f, -0.02935147f, 0.11443478f,
-       -0.05034208f, 5.65494271e-003f, 0.02076526f, -0.04577984f,
-       -0.04735741f, 0.02961071f, -0.09307127f, -0.04417921f, -0.04990027f,
-       -0.03940028f, 0.01306016f, 0.06267900f, 0.03758737f, 0.08460117f,
-       0.13858789f, 0.04862388f, -0.06319809f, -0.05655516f, 0.01885816f,
-       -0.03285607f, 0.03371567f, -0.07040928f, -0.04514049f, 0.01392166f,
-       0.08184422f, -0.07230316f, 0.02386871f, 0.02184591f, 0.02605764f,
-       -0.01033954f, 9.29878280e-003f, 7.67351175e-003f, 0.15189242f,
-       0.02069071f, -0.09738296f, -0.08894105f, -0.07768748f, 0.02332268f,
-       -0.01778995f, -0.03258888f, -0.08180822f, -0.08492987f, 0.02290156f,
-       -0.11368170f, -0.03554465f, -0.04533844f, -0.02861580f, 0.06782424f,
-       0.01113123f, 0.02453644f, 0.12721945f, 0.08084814f, -0.03607795f,
-       0.01109122f, 0.04803548f, -0.03489929f, 0.03399536f, -0.05682014f,
-       8.59533902e-003f, -4.27904585e-003f, 0.03230887f, -0.01300198f,
-       -0.01038137f, -0.07930113f, 8.33097473e-003f, 0.02296994f,
-       -0.01306500f, -0.01881626f, 0.04413369f, 0.05729880f, -0.03761553f,
-       0.01942326f, 1.64540811e-003f, -0.03811319f, 0.04190650f, -0.14978096f,
-       -0.04514487f, 0.01209545f, -5.46460645e-003f, -0.01647195f,
-       7.63064111e-003f, -0.07494587f, 0.08415288f, 0.10020141f, -0.01228561f,
-       0.06553826f, 0.04554005f, 0.07890417f, 0.03041138f, 0.01752007f,
-       0.09208256f, -3.74419295e-004f, 0.10549527f, 0.04686913f, 0.01894833f,
-       -0.02651412f, -4.34682379e-003f, 5.44942822e-003f, 0.01444484f,
-       0.05882156f, -0.03336544f, 0.04603891f, -0.10432546f, 0.01923928f,
-       0.01842845f, -0.01712168f, -0.02222766f, 0.04693324f, -0.06202956f,
-       -0.01422159f, 0.08732220f, -0.07706107f, 0.02661049f, -0.04300238f,
-       -0.03092422f, -0.03552184f, -0.01886088f, -0.04979934f, 0.03906401f,
-       0.04608644f, 0.04966111f, 0.04275464f, -0.04621769f, -0.02653212f,
-       8.57011229e-003f, 0.03839684f, 0.05818764f, 0.03880796f,
-       -2.76100676e-004f, 0.03076511f, -0.03266929f, -0.05374557f,
-       0.04986527f, -9.45429131e-003f, 0.03582499f, -2.64564669e-003f,
-       -1.07461517e-003f, 0.02962313f, -0.01483363f, 0.03060869f, 0.02448327f,
-       0.01845641f, 0.03282966f, -0.03534438f, -0.01084059f, -0.01119136f,
-       -1.85360224e-003f, -5.94652840e-004f, -0.04451817f, 2.98327743e-003f,
-       0.06272484f, -0.02152076f, -3.05971340e-003f, -0.05070828f,
-       0.01531762f, 0.01282815f, 0.05167150f, 9.46266949e-003f,
-       -3.34558333e-003f, 0.11442288f, -0.03906701f, -2.67325155e-003f,
-       0.03069184f, -0.01134165f, 0.02949462f, 0.02879886f, 0.03855566f,
-       -0.03450781f, 0.09142872f, -0.02156654f, 0.06075062f, -0.06220816f,
-       0.01944680f, 6.68372354e-003f, -0.06656796f, 8.70784000e-003f,
-       0.03456013f, 0.02434320f, -0.13236357f, -0.04177035f, -0.02069627f,
-       0.01068112f, 0.01505432f, -0.07517391f, -3.83571628e-003f,
-       -0.06298508f, -0.02881260f, -0.13101046f, -0.07221562f,
-       -5.79945277e-003f, -8.57300125e-003f, 0.03782469f, 0.02762164f,
-       0.04942456f, -0.02936396f, 0.09597211f, 0.01921411f, 0.06101191f,
-       -0.04787507f, -0.01379578f, -7.40224449e-003f, -0.02220136f,
-       -0.01313756f, 7.77558051e-003f, 0.12296968f, 0.02939998f, 0.03594062f,
-       -0.07788624f, -0.01133144f, 3.99316690e-004f, -0.06090347f,
-       -0.01122066f, -4.68682544e-003f, 0.07633100f, -0.06748922f,
-       -0.05640298f, -0.05265681f, -0.01139122f, -0.01624347f, -0.04715714f,
-       -0.01099092f, 0.01048561f, 3.28499987e-003f, -0.05810167f,
-       -0.07699911f, -0.03330683f, 0.04185145f, 0.03478536f, 0.02275165f,
-       0.02304766f, 6.66040834e-003f, 0.10968148f, -5.93013782e-003f,
-       -0.04858336f, -0.04203213f, -0.09316786f, -6.13074889e-003f,
-       -0.02544625f, 0.01366201f, 9.18555818e-003f, -0.01846578f,
-       -0.05622401f, -0.03989377f, -0.07810296f, 6.91275718e-003f,
-       0.05957597f, -0.03901334f, 0.01572002f, -0.01193903f,
-       -6.89400872e-003f, -0.03093356f, -0.04136098f, -0.01562869f,
-       -0.04604580f, 0.02865234f, -0.08678447f, -0.03232484f, -0.05364593f,
-       -0.01445016f, -0.07003860f, -0.08669746f, -0.04520775f, 0.04274122f,
-       0.03117515f, 0.08175703f, 0.01081109f, 0.06379741f, 0.06199206f,
-       0.02865988f, 0.02360346f, 0.06725410f, -0.03248780f, -9.37702879e-003f,
-       0.08265898f, -0.02245839f, 0.05125763f, -0.01862395f, 0.01973453f,
-       -0.01994494f, -0.10770868f, 0.03180375f, 3.23935156e-003f,
-       -0.02142080f, -0.04256190f, 0.04760900f, 0.04282863f, 0.05635953f,
-       -0.01870849f, 0.05540622f, -0.03042666f, 0.01455277f, -0.06630179f,
-       -0.05843807f, -0.03739681f, -0.09739155f, -0.03220233f, -0.05620182f,
-       -0.10381401f, 0.07400211f, 4.20676917e-003f, 0.03258535f,
-       2.14308966e-003f, 0.05121966f, -0.01274337f, 0.02384761f, 0.06335578f,
-       -0.07905591f, 0.08375625f, -0.07898903f, -0.06508528f, -0.02498444f,
-       0.06535810f, 0.03970535f, 0.04895468f, -0.01169566f, -0.03980601f,
-       0.05682293f, 0.05925463f, -0.01165808f, -0.07936699f, -0.04208954f,
-       0.01333987f, 0.09051196f, 0.10098671f, -0.03974256f, 0.01238771f,
-       -0.07501741f, -0.03655440f, -0.04301528f, 0.09216860f,
-       4.63579083e-004f, 0.02851115f, 0.02142735f, 1.28244064e-004f,
-       0.02879687f, -0.08554889f, -0.04838862f, 0.08135369f, -0.05756533f,
-       0.01413900f, 0.03451880f, -0.06619488f, -0.03053130f, 0.02961676f,
-       -0.07384635f, 0.01135692f, 0.05283910f, -0.07778034f, -0.02107482f,
-       -0.05511716f, -0.13473752f, 0.03030157f, 0.06722020f, -0.06218817f,
-       -0.05826827f, 0.06254654f, 0.02895772f, -0.01664000f, -0.03620280f,
-       -0.01612278f, -1.46097376e-003f, 0.14013411f, -8.96181818e-003f,
-       -0.03250246f, 3.38630192e-003f, 2.64779478e-003f, 0.03359732f,
-       -0.02411991f, -0.04229729f, 0.10666174f, -6.66579151f };
-    return std::vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
+    Mat getPeopleDetector64x128()
+    {
+        static float detector[] = {
+           0.05359386f, -0.14721455f, -0.05532170f, 0.05077307f,
+           0.11547081f, -0.04268804f, 0.04635834f, -0.05468199f, 0.08232084f,
+           0.10424068f, -0.02294518f, 0.01108519f, 0.01378693f, 0.11193510f,
+           0.01268418f, 0.08528346f, -0.06309239f, 0.13054633f, 0.08100729f,
+           -0.05209739f, -0.04315529f, 0.09341384f, 0.11035026f, -0.07596218f,
+           -0.05517511f, -0.04465296f, 0.02947334f, 0.04555536f,
+           -3.55954492e-003f, 0.07818956f, 0.07730991f, 0.07890715f, 0.06222893f,
+           0.09001380f, -0.03574381f, 0.03414327f, 0.05677258f, -0.04773581f,
+           0.03746637f, -0.03521175f, 0.06955440f, -0.03849038f, 0.01052293f,
+           0.01736112f, 0.10867710f, 0.08748853f, 3.29739624e-003f, 0.10907028f,
+           0.07913758f, 0.10393070f, 0.02091867f, 0.11594022f, 0.13182420f,
+           0.09879354f, 0.05362710f, -0.06745391f, -7.01260753e-003f,
+           5.24702156e-003f, 0.03236255f, 0.01407916f, 0.02207983f, 0.02537322f,
+           0.04547948f, 0.07200756f, 0.03129894f, -0.06274468f, 0.02107014f,
+           0.06035208f, 0.08636236f, 4.53164103e-003f, 0.02193363f, 0.02309801f,
+           0.05568166f, -0.02645093f, 0.04448695f, 0.02837519f, 0.08975694f,
+           0.04461516f, 0.08975355f, 0.07514391f, 0.02306982f, 0.10410084f,
+           0.06368385f, 0.05943464f, 4.58420580e-003f, 0.05220337f, 0.06675851f,
+           0.08358569f, 0.06712101f, 0.06559004f, -0.03930482f, -9.15936660e-003f,
+           -0.05897915f, 0.02816453f, 0.05032348f, 0.06780671f, 0.03377650f,
+           -6.09417039e-004f, -0.01795146f, -0.03083684f, -0.01302475f,
+           -0.02972313f, 7.88706727e-003f, -0.03525961f, -2.50397739e-003f,
+           0.05245084f, 0.11791293f, -0.02167498f, 0.05299332f, 0.06640524f,
+           0.05190265f, -8.27316567e-003f, 0.03033127f, 0.05842173f,
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+           0.02865988f, 0.02360346f, 0.06725410f, -0.03248780f, -9.37702879e-003f,
+           0.08265898f, -0.02245839f, 0.05125763f, -0.01862395f, 0.01973453f,
+           -0.01994494f, -0.10770868f, 0.03180375f, 3.23935156e-003f,
+           -0.02142080f, -0.04256190f, 0.04760900f, 0.04282863f, 0.05635953f,
+           -0.01870849f, 0.05540622f, -0.03042666f, 0.01455277f, -0.06630179f,
+           -0.05843807f, -0.03739681f, -0.09739155f, -0.03220233f, -0.05620182f,
+           -0.10381401f, 0.07400211f, 4.20676917e-003f, 0.03258535f,
+           2.14308966e-003f, 0.05121966f, -0.01274337f, 0.02384761f, 0.06335578f,
+           -0.07905591f, 0.08375625f, -0.07898903f, -0.06508528f, -0.02498444f,
+           0.06535810f, 0.03970535f, 0.04895468f, -0.01169566f, -0.03980601f,
+           0.05682293f, 0.05925463f, -0.01165808f, -0.07936699f, -0.04208954f,
+           0.01333987f, 0.09051196f, 0.10098671f, -0.03974256f, 0.01238771f,
+           -0.07501741f, -0.03655440f, -0.04301528f, 0.09216860f,
+           4.63579083e-004f, 0.02851115f, 0.02142735f, 1.28244064e-004f,
+           0.02879687f, -0.08554889f, -0.04838862f, 0.08135369f, -0.05756533f,
+           0.01413900f, 0.03451880f, -0.06619488f, -0.03053130f, 0.02961676f,
+           -0.07384635f, 0.01135692f, 0.05283910f, -0.07778034f, -0.02107482f,
+           -0.05511716f, -0.13473752f, 0.03030157f, 0.06722020f, -0.06218817f,
+           -0.05826827f, 0.06254654f, 0.02895772f, -0.01664000f, -0.03620280f,
+           -0.01612278f, -1.46097376e-003f, 0.14013411f, -8.96181818e-003f,
+           -0.03250246f, 3.38630192e-003f, 2.64779478e-003f, 0.03359732f,
+           -0.02411991f, -0.04229729f, 0.10666174f, -6.66579151f };
+
+        return Mat(1, static_cast<int>(sizeof(detector)/sizeof(detector[0])), CV_32FC1, detector);
+    }
 }
 
 #endif
diff --git a/modules/cudaobjdetect/test/test_objdetect.cpp b/modules/cudaobjdetect/test/test_objdetect.cpp
index 8c7b5ec918..79b1f0734f 100644
--- a/modules/cudaobjdetect/test/test_objdetect.cpp
+++ b/modules/cudaobjdetect/test/test_objdetect.cpp
@@ -48,9 +48,10 @@ using namespace cvtest;
 
 //#define DUMP
 
-struct HOG : testing::TestWithParam<cv::cuda::DeviceInfo>, cv::cuda::HOGDescriptor
+struct HOG : testing::TestWithParam<cv::cuda::DeviceInfo>
 {
     cv::cuda::DeviceInfo devInfo;
+    cv::Ptr<cv::cuda::HOG> hog;
 
 #ifdef DUMP
     std::ofstream f;
@@ -69,23 +70,13 @@ struct HOG : testing::TestWithParam<cv::cuda::DeviceInfo>, cv::cuda::HOGDescript
         devInfo = GetParam();
 
         cv::cuda::setDevice(devInfo.deviceID());
+
+        hog = cv::cuda::HOG::create();
     }
 
 #ifdef DUMP
-    void dump(const cv::Mat& blockHists, const std::vector<cv::Point>& locations)
+    void dump(const std::vector<cv::Point>& locations)
     {
-        f.write((char*)&blockHists.rows, sizeof(blockHists.rows));
-        f.write((char*)&blockHists.cols, sizeof(blockHists.cols));
-
-        for (int i = 0; i < blockHists.rows; ++i)
-        {
-            for (int j = 0; j < blockHists.cols; ++j)
-            {
-                float val = blockHists.at<float>(i, j);
-                f.write((char*)&val, sizeof(val));
-            }
-        }
-
         int nlocations = locations.size();
         f.write((char*)&nlocations, sizeof(nlocations));
 
@@ -93,21 +84,18 @@ struct HOG : testing::TestWithParam<cv::cuda::DeviceInfo>, cv::cuda::HOGDescript
             f.write((char*)&locations[i], sizeof(locations[i]));
     }
 #else
-    void compare(const cv::Mat& blockHists, const std::vector<cv::Point>& locations)
+    void compare(const std::vector<cv::Point>& locations)
     {
+        // skip block_hists check
         int rows, cols;
         f.read((char*)&rows, sizeof(rows));
         f.read((char*)&cols, sizeof(cols));
-        ASSERT_EQ(rows, blockHists.rows);
-        ASSERT_EQ(cols, blockHists.cols);
-
-        for (int i = 0; i < blockHists.rows; ++i)
+        for (int i = 0; i < rows; ++i)
         {
-            for (int j = 0; j < blockHists.cols; ++j)
+            for (int j = 0; j < cols; ++j)
             {
                 float val;
                 f.read((char*)&val, sizeof(val));
-                ASSERT_NEAR(val, blockHists.at<float>(i, j), 1e-3);
             }
         }
 
@@ -126,54 +114,41 @@ struct HOG : testing::TestWithParam<cv::cuda::DeviceInfo>, cv::cuda::HOGDescript
 
     void testDetect(const cv::Mat& img)
     {
-        gamma_correction = false;
-        setSVMDetector(cv::cuda::HOGDescriptor::getDefaultPeopleDetector());
+        hog->setGammaCorrection(false);
+        hog->setSVMDetector(hog->getDefaultPeopleDetector());
 
         std::vector<cv::Point> locations;
 
         // Test detect
-        detect(loadMat(img), locations, 0);
+        hog->detect(loadMat(img), locations);
 
 #ifdef DUMP
-        dump(cv::Mat(block_hists), locations);
+        dump(locations);
 #else
-        compare(cv::Mat(block_hists), locations);
+        compare(locations);
 #endif
 
         // Test detect on smaller image
         cv::Mat img2;
         cv::resize(img, img2, cv::Size(img.cols / 2, img.rows / 2));
-        detect(loadMat(img2), locations, 0);
+        hog->detect(loadMat(img2), locations);
 
 #ifdef DUMP
-        dump(cv::Mat(block_hists), locations);
+        dump(locations);
 #else
-        compare(cv::Mat(block_hists), locations);
+        compare(locations);
 #endif
 
         // Test detect on greater image
         cv::resize(img, img2, cv::Size(img.cols * 2, img.rows * 2));
-        detect(loadMat(img2), locations, 0);
+        hog->detect(loadMat(img2), locations);
 
 #ifdef DUMP
-        dump(cv::Mat(block_hists), locations);
+        dump(locations);
 #else
-        compare(cv::Mat(block_hists), locations);
+        compare(locations);
 #endif
     }
-
-    // Does not compare border value, as interpolation leads to delta
-    void compare_inner_parts(cv::Mat d1, cv::Mat d2)
-    {
-        for (int i = 1; i < blocks_per_win_y - 1; ++i)
-            for (int j = 1; j < blocks_per_win_x - 1; ++j)
-                for (int k = 0; k < block_hist_size; ++k)
-                {
-                    float a = d1.at<float>(0, (i * blocks_per_win_x + j) * block_hist_size);
-                    float b = d2.at<float>(0, (i * blocks_per_win_x + j) * block_hist_size);
-                    ASSERT_FLOAT_EQ(a, b);
-                }
-    }
 };
 
 // desabled while resize does not fixed
@@ -182,13 +157,8 @@ CUDA_TEST_P(HOG, DISABLED_Detect)
     cv::Mat img_rgb = readImage("hog/road.png");
     ASSERT_FALSE(img_rgb.empty());
 
-#ifdef DUMP
     f.open((std::string(cvtest::TS::ptr()->get_data_path()) + "hog/expected_output.bin").c_str(), std::ios_base::binary);
     ASSERT_TRUE(f.is_open());
-#else
-    f.open((std::string(cvtest::TS::ptr()->get_data_path()) + "hog/expected_output.bin").c_str(), std::ios_base::binary);
-    ASSERT_TRUE(f.is_open());
-#endif
 
     // Test on color image
     cv::Mat img;
@@ -198,8 +168,6 @@ CUDA_TEST_P(HOG, DISABLED_Detect)
     // Test on gray image
     cv::cvtColor(img_rgb, img, cv::COLOR_BGR2GRAY);
     testDetect(img);
-
-    f.close();
 }
 
 CUDA_TEST_P(HOG, GetDescriptors)
@@ -216,8 +184,14 @@ CUDA_TEST_P(HOG, GetDescriptors)
 
     // Convert train images into feature vectors (train table)
     cv::cuda::GpuMat descriptors, descriptors_by_cols;
-    getDescriptors(d_img, win_size, descriptors, DESCR_FORMAT_ROW_BY_ROW);
-    getDescriptors(d_img, win_size, descriptors_by_cols, DESCR_FORMAT_COL_BY_COL);
+
+    hog->setWinStride(Size(64, 128));
+
+    hog->setDescriptorFormat(cv::cuda::HOG::DESCR_FORMAT_ROW_BY_ROW);
+    hog->compute(d_img, descriptors);
+
+    hog->setDescriptorFormat(cv::cuda::HOG::DESCR_FORMAT_COL_BY_COL);
+    hog->compute(d_img, descriptors_by_cols);
 
     // Check size of the result train table
     wins_per_img_x = 3;
@@ -242,48 +216,6 @@ CUDA_TEST_P(HOG, GetDescriptors)
                     ASSERT_EQ(l[(y * blocks_per_win_x + x) * block_hist_size + k],
                               r[(x * blocks_per_win_y + y) * block_hist_size + k]);
     }
-
-    /* Now we want to extract the same feature vectors, but from single images. NOTE: results will
-    be defferent, due to border values interpolation. Using of many small images is slower, however we
-    wont't call getDescriptors and will use computeBlockHistograms instead of. computeBlockHistograms
-    works good, it can be checked in the gpu_hog sample */
-
-    img_rgb = readImage("hog/positive1.png");
-    ASSERT_TRUE(!img_rgb.empty());
-    cv::cvtColor(img_rgb, img, cv::COLOR_BGR2BGRA);
-    computeBlockHistograms(cv::cuda::GpuMat(img));
-    // Everything is fine with interpolation for left top subimage
-    ASSERT_EQ(0.0, cv::norm((cv::Mat)block_hists, (cv::Mat)descriptors.rowRange(0, 1)));
-
-    img_rgb = readImage("hog/positive2.png");
-    ASSERT_TRUE(!img_rgb.empty());
-    cv::cvtColor(img_rgb, img, cv::COLOR_BGR2BGRA);
-    computeBlockHistograms(cv::cuda::GpuMat(img));
-    compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(1, 2)));
-
-    img_rgb = readImage("hog/negative1.png");
-    ASSERT_TRUE(!img_rgb.empty());
-    cv::cvtColor(img_rgb, img, cv::COLOR_BGR2BGRA);
-    computeBlockHistograms(cv::cuda::GpuMat(img));
-    compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(2, 3)));
-
-    img_rgb = readImage("hog/negative2.png");
-    ASSERT_TRUE(!img_rgb.empty());
-    cv::cvtColor(img_rgb, img, cv::COLOR_BGR2BGRA);
-    computeBlockHistograms(cv::cuda::GpuMat(img));
-    compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(3, 4)));
-
-    img_rgb = readImage("hog/positive3.png");
-    ASSERT_TRUE(!img_rgb.empty());
-    cv::cvtColor(img_rgb, img, cv::COLOR_BGR2BGRA);
-    computeBlockHistograms(cv::cuda::GpuMat(img));
-    compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(4, 5)));
-
-    img_rgb = readImage("hog/negative3.png");
-    ASSERT_TRUE(!img_rgb.empty());
-    cv::cvtColor(img_rgb, img, cv::COLOR_BGR2BGRA);
-    computeBlockHistograms(cv::cuda::GpuMat(img));
-    compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(5, 6)));
 }
 
 INSTANTIATE_TEST_CASE_P(CUDA_ObjDetect, HOG, ALL_DEVICES);
@@ -310,12 +242,12 @@ CUDA_TEST_P(CalTech, HOG)
     cv::cuda::GpuMat d_img(img);
     cv::Mat markedImage(img.clone());
 
-    cv::cuda::HOGDescriptor d_hog;
-    d_hog.setSVMDetector(cv::cuda::HOGDescriptor::getDefaultPeopleDetector());
-    d_hog.nlevels = d_hog.nlevels + 32;
+    cv::Ptr<cv::cuda::HOG> d_hog = cv::cuda::HOG::create();
+    d_hog->setSVMDetector(d_hog->getDefaultPeopleDetector());
+    d_hog->setNumLevels(d_hog->getNumLevels() + 32);
 
     std::vector<cv::Rect> found_locations;
-    d_hog.detectMultiScale(d_img, found_locations);
+    d_hog->detectMultiScale(d_img, found_locations);
 
 #if defined (LOG_CASCADE_STATISTIC)
     for (int i = 0; i < (int)found_locations.size(); i++)
@@ -326,7 +258,8 @@ CUDA_TEST_P(CalTech, HOG)
         cv::rectangle(markedImage, r , CV_RGB(255, 0, 0));
     }
 
-    cv::imshow("Res", markedImage); cv::waitKey();
+    cv::imshow("Res", markedImage);
+    cv::waitKey();
 #endif
 }
 
diff --git a/samples/gpu/hog.cpp b/samples/gpu/hog.cpp
index 106de79320..01978a21c9 100644
--- a/samples/gpu/hog.cpp
+++ b/samples/gpu/hog.cpp
@@ -244,19 +244,13 @@ void App::run()
     Size win_size(args.win_width, args.win_width * 2); //(64, 128) or (48, 96)
     Size win_stride(args.win_stride_width, args.win_stride_height);
 
+    cv::Ptr<cv::cuda::HOG> gpu_hog = cv::cuda::HOG::create(win_size);
+    cv::HOGDescriptor cpu_hog(win_size, Size(16, 16), Size(8, 8), Size(8, 8), 9);
+
     // Create HOG descriptors and detectors here
-    vector<float> detector;
-    if (win_size == Size(64, 128))
-        detector = cv::cuda::HOGDescriptor::getPeopleDetector64x128();
-    else
-        detector = cv::cuda::HOGDescriptor::getPeopleDetector48x96();
-
-    cv::cuda::HOGDescriptor gpu_hog(win_size, Size(16, 16), Size(8, 8), Size(8, 8), 9,
-                                   cv::cuda::HOGDescriptor::DEFAULT_WIN_SIGMA, 0.2, gamma_corr,
-                                   cv::cuda::HOGDescriptor::DEFAULT_NLEVELS);
-    cv::HOGDescriptor cpu_hog(win_size, Size(16, 16), Size(8, 8), Size(8, 8), 9, 1, -1,
-                              HOGDescriptor::L2Hys, 0.2, gamma_corr, cv::HOGDescriptor::DEFAULT_NLEVELS);
-    gpu_hog.setSVMDetector(detector);
+    Mat detector = gpu_hog->getDefaultPeopleDetector();
+
+    gpu_hog->setSVMDetector(detector);
     cpu_hog.setSVMDetector(detector);
 
     while (running)
@@ -307,9 +301,6 @@ void App::run()
             else img = img_aux;
             img_to_show = img;
 
-            gpu_hog.nlevels = nlevels;
-            cpu_hog.nlevels = nlevels;
-
             vector<Rect> found;
 
             // Perform HOG classification
@@ -317,11 +308,19 @@ void App::run()
             if (use_gpu)
             {
                 gpu_img.upload(img);
-                gpu_hog.detectMultiScale(gpu_img, found, hit_threshold, win_stride,
-                                         Size(0, 0), scale, gr_threshold);
+                gpu_hog->setNumLevels(nlevels);
+                gpu_hog->setHitThreshold(hit_threshold);
+                gpu_hog->setWinStride(win_stride);
+                gpu_hog->setScaleFactor(scale);
+                gpu_hog->setGroupThreshold(gr_threshold);
+                gpu_hog->detectMultiScale(gpu_img, found);
             }
-            else cpu_hog.detectMultiScale(img, found, hit_threshold, win_stride,
+            else
+            {
+                cpu_hog.nlevels = nlevels;
+                cpu_hog.detectMultiScale(img, found, hit_threshold, win_stride,
                                           Size(0, 0), scale, gr_threshold);
+            }
             hogWorkEnd();
 
             // Draw positive classified windows