diff --git a/samples/dnn/dasiamrpn_tracker.cpp b/samples/dnn/dasiamrpn_tracker.cpp
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+++ b/samples/dnn/dasiamrpn_tracker.cpp
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+// DaSiamRPN tracker.
+// Original paper: https://arxiv.org/abs/1808.06048
+// Link to original repo: https://github.com/foolwood/DaSiamRPN
+// Links to onnx models:
+// - network:     https://www.dropbox.com/s/rr1lk9355vzolqv/dasiamrpn_model.onnx?dl=0
+// - kernel_r1:   https://www.dropbox.com/s/999cqx5zrfi7w4p/dasiamrpn_kernel_r1.onnx?dl=0
+// - kernel_cls1: https://www.dropbox.com/s/qvmtszx5h339a0w/dasiamrpn_kernel_cls1.onnx?dl=0
+
+#include <iostream>
+#include <cmath>
+
+#include <opencv2/dnn.hpp>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/highgui.hpp>
+
+using namespace cv;
+using namespace cv::dnn;
+
+const char *keys =
+        "{ help     h  |   | Print help message }"
+        "{ input    i  |   | Full path to input video folder, the specific camera index. (empty for camera 0) }"
+        "{ net         | dasiamrpn_model.onnx | Path to onnx model of net}"
+        "{ kernel_cls1 | dasiamrpn_kernel_cls1.onnx | Path to onnx model of kernel_r1 }"
+        "{ kernel_r1   | dasiamrpn_kernel_r1.onnx | Path to onnx model of kernel_cls1 }"
+        "{ backend     | 0 | Choose one of computation backends: "
+                            "0: automatically (by default), "
+                            "1: Halide language (http://halide-lang.org/), "
+                            "2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
+                            "3: OpenCV implementation }"
+        "{ target      | 0 | Choose one of target computation devices: "
+                            "0: CPU target (by default), "
+                            "1: OpenCL, "
+                            "2: OpenCL fp16 (half-float precision), "
+                            "3: VPU }"
+;
+
+// Initial parameters of the model
+struct trackerConfig
+{
+    float windowInfluence = 0.43f;
+    float lr = 0.4f;
+    int scale = 8;
+    bool swapRB = false;
+    int totalStride = 8;
+    float penaltyK = 0.055f;
+    int exemplarSize = 127;
+    int instanceSize = 271;
+    float contextAmount = 0.5f;
+    std::vector<float> ratios = { 0.33f, 0.5f, 1.0f, 2.0f, 3.0f };
+    int anchorNum = int(ratios.size());
+    Mat anchors;
+    Mat windows;
+    Scalar avgChans;
+    Size imgSize = { 0, 0 };
+    Rect2f targetBox = { 0, 0, 0, 0 };
+    int scoreSize = (instanceSize - exemplarSize) / totalStride + 1;
+
+    void update_scoreSize()
+    {
+        scoreSize = int((instanceSize - exemplarSize) / totalStride + 1);
+    }
+};
+
+static void softmax(const Mat& src, Mat& dst);
+static void elementMax(Mat& src);
+static Mat generateHanningWindow(const trackerConfig& trackState);
+static Mat generateAnchors(trackerConfig& trackState);
+static Mat getSubwindow(Mat& img, const Rect2f& targetBox, float originalSize, Scalar avgChans);
+static float trackerEval(Mat img, trackerConfig& trackState, Net& siamRPN);
+static void trackerInit(Mat img, trackerConfig& trackState, Net& siamRPN, Net& siamKernelR1, Net& siamKernelCL1);
+
+template <typename T> static
+T sizeCal(const T& w, const T& h)
+{
+    T pad = (w + h) * T(0.5);
+    T sz2 = (w + pad) * (h + pad);
+    return sqrt(sz2);
+}
+
+template <>
+Mat sizeCal(const Mat& w, const Mat& h)
+{
+    Mat pad = (w + h) * 0.5;
+    Mat sz2 = (w + pad).mul((h + pad));
+
+    cv::sqrt(sz2, sz2);
+    return sz2;
+}
+
+static
+int run(int argc, char** argv)
+{
+    // Parse command line arguments.
+    CommandLineParser parser(argc, argv, keys);
+
+    if (parser.has("help"))
+    {
+        parser.printMessage();
+        return 0;
+    }
+
+    std::string inputName = parser.get<String>("input");
+    std::string net = parser.get<String>("net");
+    std::string kernel_cls1 = parser.get<String>("kernel_cls1");
+    std::string kernel_r1 = parser.get<String>("kernel_r1");
+    int backend = parser.get<int>("backend");
+    int target = parser.get<int>("target");
+
+    // Read nets.
+    Net siamRPN, siamKernelCL1, siamKernelR1;
+    try
+    {
+        siamRPN = readNet(samples::findFile(net));
+        siamKernelCL1 = readNet(samples::findFile(kernel_cls1));
+        siamKernelR1 = readNet(samples::findFile(kernel_r1));
+    }
+    catch (const cv::Exception& ee)
+    {
+        std::cerr << "Exception: " << ee.what() << std::endl;
+        std::cout << "Can't load the network by using the following files:" << std::endl;
+        std::cout << "siamRPN : " << net << std::endl;
+        std::cout << "siamKernelCL1 : " << kernel_cls1 << std::endl;
+        std::cout << "siamKernelR1 : " << kernel_r1 << std::endl;
+        return 2;
+    }
+
+    // Set model backend.
+    siamRPN.setPreferableBackend(backend);
+    siamRPN.setPreferableTarget(target);
+    siamKernelR1.setPreferableBackend(backend);
+    siamKernelR1.setPreferableTarget(target);
+    siamKernelCL1.setPreferableBackend(backend);
+    siamKernelCL1.setPreferableTarget(target);
+
+    const std::string winName = "DaSiamRPN";
+    namedWindow(winName, WINDOW_AUTOSIZE);
+
+    // Open a video file or an image file or a camera stream.
+    VideoCapture cap;
+
+    if (inputName.empty() || (isdigit(inputName[0]) && inputName.size() == 1))
+    {
+        int c = inputName.empty() ? 0 : inputName[0] - '0';
+        std::cout << "Trying to open camera #" << c << " ..." << std::endl;
+        if (!cap.open(c))
+        {
+            std::cout << "Capture from camera #" << c << " didn't work. Specify -i=<video> parameter to read from video file" << std::endl;
+            return 2;
+        }
+    }
+    else if (inputName.size())
+    {
+        inputName = samples::findFileOrKeep(inputName);
+        if (!cap.open(inputName))
+        {
+            std::cout << "Could not open: " << inputName << std::endl;
+            return 2;
+        }
+    }
+
+    // Read the first image.
+    Mat image;
+    cap >> image;
+    if (image.empty())
+    {
+        std::cerr << "Can't capture frame!" << std::endl;
+        return 2;
+    }
+
+    Mat image_select = image.clone();
+    putText(image_select, "Select initial bounding box you want to track.", Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
+    putText(image_select, "And Press the ENTER key.", Point(0, 35), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
+
+    Rect selectRect = selectROI(winName, image_select);
+    std::cout << "ROI=" << selectRect << std::endl;
+
+    trackerConfig trackState;
+    trackState.update_scoreSize();
+    trackState.targetBox = Rect2f(
+        float(selectRect.x) + float(selectRect.width) * 0.5f,  // FIXIT don't use center in Rect structures, it is confusing
+        float(selectRect.y) + float(selectRect.height) * 0.5f,
+        float(selectRect.width),
+        float(selectRect.height)
+    );
+
+    // Set tracking template.
+    trackerInit(image, trackState, siamRPN, siamKernelR1, siamKernelCL1);
+
+    TickMeter tickMeter;
+
+    for (int count = 0; ; ++count)
+    {
+        cap >> image;
+        if (image.empty())
+        {
+            std::cerr << "Can't capture frame " << count << ". End of video stream?" << std::endl;
+            break;
+        }
+
+        tickMeter.start();
+        float score = trackerEval(image, trackState, siamRPN);
+        tickMeter.stop();
+
+        Rect rect = {
+            int(trackState.targetBox.x - int(trackState.targetBox.width / 2)),
+            int(trackState.targetBox.y - int(trackState.targetBox.height / 2)),
+            int(trackState.targetBox.width),
+            int(trackState.targetBox.height)
+        };
+        std::cout << "frame " << count <<
+            ": predicted score=" << score <<
+            "  rect=" << rect <<
+            "  time=" << tickMeter.getTimeMilli() << "ms" <<
+            std::endl;
+
+        Mat render_image = image.clone();
+        rectangle(render_image, rect, Scalar(0, 255, 0), 2);
+
+        std::string timeLabel = format("Inference time: %.2f ms", tickMeter.getTimeMilli());
+        std::string scoreLabel = format("Score: %f", score);
+        putText(render_image, timeLabel, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
+        putText(render_image, scoreLabel, Point(0, 35), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
+
+        imshow(winName, render_image);
+
+        tickMeter.reset();
+
+        int c = waitKey(1);
+        if (c == 27 /*ESC*/)
+            break;
+    }
+
+    std::cout << "Exit" << std::endl;
+    return 0;
+}
+
+Mat generateHanningWindow(const trackerConfig& trackState)
+{
+    Mat baseWindows, HanningWindows;
+
+    createHanningWindow(baseWindows, Size(trackState.scoreSize, trackState.scoreSize), CV_32F);
+    baseWindows = baseWindows.reshape(0, { 1, trackState.scoreSize, trackState.scoreSize });
+    HanningWindows = baseWindows.clone();
+    for (int i = 1; i < trackState.anchorNum; i++)
+    {
+        HanningWindows.push_back(baseWindows);
+    }
+
+    return HanningWindows;
+}
+
+Mat generateAnchors(trackerConfig& trackState)
+{
+    int totalStride = trackState.totalStride, scales = trackState.scale, scoreSize = trackState.scoreSize;
+    std::vector<float> ratios = trackState.ratios;
+    std::vector<Rect2f> baseAnchors;
+    int anchorNum = int(ratios.size());
+    int size = totalStride * totalStride;
+
+    float ori = -(float(scoreSize / 2)) * float(totalStride);
+
+    for (auto i = 0; i < anchorNum; i++)
+    {
+        int ws = int(sqrt(size / ratios[i]));
+        int hs = int(ws * ratios[i]);
+
+        float wws = float(ws) * scales;
+        float hhs = float(hs) * scales;
+        Rect2f anchor = { 0, 0, wws, hhs };
+        baseAnchors.push_back(anchor);
+    }
+
+    int anchorIndex[] = { 0, 0, 0, 0 };
+    const int sizes[] = { 4, (int)ratios.size(), scoreSize, scoreSize };
+    Mat anchors(4, sizes, CV_32F);
+
+    for (auto i = 0; i < scoreSize; i++)
+    {
+        for (auto j = 0; j < scoreSize; j++)
+        {
+            for (auto k = 0; k < anchorNum; k++)
+            {
+                anchorIndex[0] = 1, anchorIndex[1] = k, anchorIndex[2] = i, anchorIndex[3] = j;
+                anchors.at<float>(anchorIndex) = ori + totalStride * i;
+
+                anchorIndex[0] = 0;
+                anchors.at<float>(anchorIndex) = ori + totalStride * j;
+
+                anchorIndex[0] = 2;
+                anchors.at<float>(anchorIndex) = baseAnchors[k].width;
+
+                anchorIndex[0] = 3;
+                anchors.at<float>(anchorIndex) = baseAnchors[k].height;
+            }
+        }
+    }
+
+    return anchors;
+}
+
+Mat getSubwindow(Mat& img, const Rect2f& targetBox, float originalSize, Scalar avgChans)
+{
+    Mat zCrop, dst;
+    Size imgSize = img.size();
+    float c = (originalSize + 1) / 2;
+    float xMin = (float)cvRound(targetBox.x - c);
+    float xMax = xMin + originalSize - 1;
+    float yMin = (float)cvRound(targetBox.y - c);
+    float yMax = yMin + originalSize - 1;
+
+    int leftPad = (int)(fmax(0., -xMin));
+    int topPad = (int)(fmax(0., -yMin));
+    int rightPad = (int)(fmax(0., xMax - imgSize.width + 1));
+    int bottomPad = (int)(fmax(0., yMax - imgSize.height + 1));
+
+    xMin = xMin + leftPad;
+    xMax = xMax + leftPad;
+    yMax = yMax + topPad;
+    yMin = yMin + topPad;
+
+    if (topPad == 0 && bottomPad == 0 && leftPad == 0 && rightPad == 0)
+    {
+        img(Rect(int(xMin), int(yMin), int(xMax - xMin + 1), int(yMax - yMin + 1))).copyTo(zCrop);
+    }
+    else
+    {
+        copyMakeBorder(img, dst, topPad, bottomPad, leftPad, rightPad, BORDER_CONSTANT, avgChans);
+        dst(Rect(int(xMin), int(yMin), int(xMax - xMin + 1), int(yMax - yMin + 1))).copyTo(zCrop);
+    }
+
+    return zCrop;
+}
+
+void softmax(const Mat& src, Mat& dst)
+{
+    Mat maxVal;
+    cv::max(src.row(1), src.row(0), maxVal);
+
+    src.row(1) -= maxVal;
+    src.row(0) -= maxVal;
+
+    exp(src, dst);
+
+    Mat sumVal = dst.row(0) + dst.row(1);
+    dst.row(0) = dst.row(0) / sumVal;
+    dst.row(1) = dst.row(1) / sumVal;
+}
+
+void elementMax(Mat& src)
+{
+    int* p = src.size.p;
+    int index[] = { 0, 0, 0, 0 };
+    for (int n = 0; n < *p; n++)
+    {
+        for (int k = 0; k < *(p + 1); k++)
+        {
+            for (int i = 0; i < *(p + 2); i++)
+            {
+                for (int j = 0; j < *(p + 3); j++)
+                {
+                    index[0] = n, index[1] = k, index[2] = i, index[3] = j;
+                    float& v = src.at<float>(index);
+                    v = fmax(v, 1.0f / v);
+                }
+            }
+        }
+    }
+}
+
+float trackerEval(Mat img, trackerConfig& trackState, Net& siamRPN)
+{
+    Rect2f targetBox = trackState.targetBox;
+
+    float wc = targetBox.height + trackState.contextAmount * (targetBox.width + targetBox.height);
+    float hc = targetBox.width + trackState.contextAmount * (targetBox.width + targetBox.height);
+
+    float sz = sqrt(wc * hc);
+    float scaleZ = trackState.exemplarSize / sz;
+
+    float searchSize = float((trackState.instanceSize - trackState.exemplarSize) / 2);
+    float pad = searchSize / scaleZ;
+    float sx = sz + 2 * pad;
+
+    Mat xCrop = getSubwindow(img, targetBox, (float)cvRound(sx), trackState.avgChans);
+
+    static Mat blob;
+    std::vector<Mat> outs;
+    std::vector<String> outNames;
+    Mat delta, score;
+    Mat sc, rc, penalty, pscore;
+
+    blobFromImage(xCrop, blob, 1.0, Size(trackState.instanceSize, trackState.instanceSize), Scalar(), trackState.swapRB, false, CV_32F);
+
+    siamRPN.setInput(blob);
+
+    outNames = siamRPN.getUnconnectedOutLayersNames();
+    siamRPN.forward(outs, outNames);
+
+    delta = outs[0];
+    score = outs[1];
+
+    score = score.reshape(0, { 2, trackState.anchorNum, trackState.scoreSize, trackState.scoreSize });
+    delta = delta.reshape(0, { 4, trackState.anchorNum, trackState.scoreSize, trackState.scoreSize });
+
+    softmax(score, score);
+
+    targetBox.width *= scaleZ;
+    targetBox.height *= scaleZ;
+
+    score = score.row(1);
+    score = score.reshape(0, { 5, 19, 19 });
+
+    // Post processing
+    delta.row(0) = delta.row(0).mul(trackState.anchors.row(2)) + trackState.anchors.row(0);
+    delta.row(1) = delta.row(1).mul(trackState.anchors.row(3)) + trackState.anchors.row(1);
+    exp(delta.row(2), delta.row(2));
+    delta.row(2) = delta.row(2).mul(trackState.anchors.row(2));
+    exp(delta.row(3), delta.row(3));
+    delta.row(3) = delta.row(3).mul(trackState.anchors.row(3));
+
+    sc = sizeCal(delta.row(2), delta.row(3)) / sizeCal(targetBox.width, targetBox.height);
+    elementMax(sc);
+
+    rc = delta.row(2).mul(1 / delta.row(3));
+    rc = (targetBox.width / targetBox.height) / rc;
+    elementMax(rc);
+
+    // Calculating the penalty
+    exp(((rc.mul(sc) - 1.) * trackState.penaltyK * (-1.0)), penalty);
+    penalty = penalty.reshape(0, { trackState.anchorNum, trackState.scoreSize, trackState.scoreSize });
+
+    pscore = penalty.mul(score);
+    pscore = pscore * (1.0 - trackState.windowInfluence) + trackState.windows * trackState.windowInfluence;
+
+    int bestID[] = { 0 };
+    // Find the index of best score.
+    minMaxIdx(pscore.reshape(0, { trackState.anchorNum * trackState.scoreSize * trackState.scoreSize, 1 }), 0, 0, 0, bestID);
+    delta = delta.reshape(0, { 4, trackState.anchorNum * trackState.scoreSize * trackState.scoreSize });
+    penalty = penalty.reshape(0, { trackState.anchorNum * trackState.scoreSize * trackState.scoreSize, 1 });
+    score = score.reshape(0, { trackState.anchorNum * trackState.scoreSize * trackState.scoreSize, 1 });
+
+    int index[] = { 0, bestID[0] };
+    Rect2f resBox = { 0, 0, 0, 0 };
+
+    resBox.x = delta.at<float>(index) / scaleZ;
+    index[0] = 1;
+    resBox.y = delta.at<float>(index) / scaleZ;
+    index[0] = 2;
+    resBox.width = delta.at<float>(index) / scaleZ;
+    index[0] = 3;
+    resBox.height = delta.at<float>(index) / scaleZ;
+
+    float lr = penalty.at<float>(bestID) * score.at<float>(bestID) * trackState.lr;
+
+    resBox.x = resBox.x + targetBox.x;
+    resBox.y = resBox.y + targetBox.y;
+    targetBox.width /= scaleZ;
+    targetBox.height /= scaleZ;
+
+    resBox.width = targetBox.width * (1 - lr) + resBox.width * lr;
+    resBox.height = targetBox.height * (1 - lr) + resBox.height * lr;
+
+    resBox.x = float(fmax(0., fmin(float(trackState.imgSize.width), resBox.x)));
+    resBox.y = float(fmax(0., fmin(float(trackState.imgSize.height), resBox.y)));
+    resBox.width = float(fmax(10., fmin(float(trackState.imgSize.width), resBox.width)));
+    resBox.height = float(fmax(10., fmin(float(trackState.imgSize.height), resBox.height)));
+
+    trackState.targetBox = resBox;
+    return score.at<float>(bestID);
+}
+
+void trackerInit(Mat img, trackerConfig& trackState, Net& siamRPN, Net& siamKernelR1, Net& siamKernelCL1)
+{
+    Rect2f targetBox = trackState.targetBox;
+    Mat anchors = generateAnchors(trackState);
+    trackState.anchors = anchors;
+
+    Mat windows = generateHanningWindow(trackState);
+
+    trackState.windows = windows;
+    trackState.imgSize = img.size();
+
+    trackState.avgChans = mean(img);
+    float wc = targetBox.width + trackState.contextAmount * (targetBox.width + targetBox.height);
+    float hc = targetBox.height + trackState.contextAmount * (targetBox.width + targetBox.height);
+    float sz = (float)cvRound(sqrt(wc * hc));
+
+    Mat zCrop = getSubwindow(img, targetBox, sz, trackState.avgChans);
+    static Mat blob;
+
+    blobFromImage(zCrop, blob, 1.0, Size(trackState.exemplarSize, trackState.exemplarSize), Scalar(), trackState.swapRB, false, CV_32F);
+    siamRPN.setInput(blob);
+    Mat out1;
+    siamRPN.forward(out1, "63");
+
+    siamKernelCL1.setInput(out1);
+    siamKernelR1.setInput(out1);
+
+    Mat cls1 = siamKernelCL1.forward();
+    Mat r1 = siamKernelR1.forward();
+    std::vector<int> r1_shape = { 20, 256, 4, 4 }, cls1_shape = { 10, 256, 4, 4 };
+
+    siamRPN.setParam(siamRPN.getLayerId("65"), 0, r1.reshape(0, r1_shape));
+    siamRPN.setParam(siamRPN.getLayerId("68"), 0, cls1.reshape(0, cls1_shape));
+}
+
+int main(int argc, char **argv)
+{
+    try
+    {
+        return run(argc, argv);
+    }
+    catch (const std::exception& e)
+    {
+        std::cerr << "FATAL: C++ exception: " << e.what() << std::endl;
+        return 1;
+    }
+}