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+ add KCF Tracker, initial commit, added: tutorial, trackerKCF.cpp, modified: tracker.cpp, tracker.hpp

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Kurnianggoro 10 years ago
parent 36fbabf267
commit 43e71214c8
4 changed files with 640 additions and 0 deletions
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  1. 135
      modules/tracking/doc/[Tutorial] Adding new Tracker Method for dummies
  2. 32
      modules/tracking/include/opencv2/tracking/tracker.hpp
  3. 1
      modules/tracking/src/tracker.cpp
  4. 472
      modules/tracking/src/trackerKCF.cpp

135
modules/tracking/doc/[Tutorial] Adding new Tracker Method for dummies
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@ -0,0 +1,135 @@
/*---------------STEP 1---------------------*/
/* modify this file
* opencv2/tracking/tracker.hpp
* and put several lines of snippet similar to
* the following:
*/
/*------------------------------------------*/
class CV_EXPORTS_W TrackerKCF : public Tracker
{
public:
struct CV_EXPORTS Params
{
Params();
void read( const FileNode& /*fn*/ );
void write( FileStorage& /*fs*/ ) const;
};
/** @brief Constructor
@param parameters KCF parameters TrackerKCF::Params
*/
BOILERPLATE_CODE("KCF",TrackerKCF);
};
/*---------------STEP 2---------------------*/
/* modify this file
* src/tracker.cpp
* add one line in function
* Ptr<Tracker> Tracker::create( const String& trackerType )
*/
/*------------------------------------------*/
Ptr<Tracker> Tracker::create( const String& trackerType )
{
BOILERPLATE_CODE("MIL",TrackerMIL);
BOILERPLATE_CODE("BOOSTING",TrackerBoosting);
BOILERPLATE_CODE("MEDIANFLOW",TrackerMedianFlow);
BOILERPLATE_CODE("TLD",TrackerTLD);
BOILERPLATE_CODE("KCF",TrackerKCF); // add this line!
return Ptr<Tracker>();
}
/*---------------STEP 3---------------------*/
/* make a new file and paste the snippet below
* and modify it according to your needs.
* also make sure to put the LICENSE part.
* src/trackerKCF.cpp
*/
/*------------------------------------------*/
/*---------------------------
| TrackerKCFModel
|---------------------------*/
namespace cv{
/**
* \brief Implementation of TrackerModel for MIL algorithm
*/
class TrackerKCFModel : public TrackerModel{
public:
TrackerKCFModel(TrackerKCF::Params /*params*/){}
~TrackerKCFModel(){}
protected:
void modelEstimationImpl( const std::vector<Mat>& responses ){}
void modelUpdateImpl(){}
};
} /* namespace cv */
/*---------------------------
| TrackerKCF
|---------------------------*/
namespace cv{
/*
* Prototype
*/
class TrackerKCFImpl : public TrackerKCF{
public:
TrackerKCFImpl( const TrackerKCF::Params &parameters = TrackerKCF::Params() );
void read( const FileNode& fn );
void write( FileStorage& fs ) const;
protected:
bool initImpl( const Mat& image, const Rect2d& boundingBox );
bool updateImpl( const Mat& image, Rect2d& boundingBox );
TrackerKCF::Params params;
};
/*
* Constructor
*/
Ptr<TrackerKCF> TrackerKCF::createTracker(const TrackerKCF::Params &parameters){
return Ptr<TrackerKCFImpl>(new TrackerKCFImpl(parameters));
}
TrackerKCFImpl::TrackerKCFImpl( const TrackerKCF::Params &parameters ) :
params( parameters )
{
isInit = false;
}
void TrackerKCFImpl::read( const cv::FileNode& fn ){
params.read( fn );
}
void TrackerKCFImpl::write( cv::FileStorage& fs ) const{
params.write( fs );
}
bool TrackerKCFImpl::initImpl( const Mat& image, const Rect2d& boundingBox ){
model=Ptr<TrackerKCFModel>(new TrackerKCFModel(params));
return true;
}
bool TrackerKCFImpl::updateImpl( const Mat& image, Rect2d& boundingBox ){return true;}
/*
* Parameters
*/
TrackerKCF::Params::Params(){
}
void TrackerKCF::Params::read( const cv::FileNode& fn ){
}
void TrackerKCF::Params::write( cv::FileStorage& fs ) const{
}
} /* namespace cv */

32
modules/tracking/include/opencv2/tracking/tracker.hpp
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@ -1189,6 +1189,38 @@ class CV_EXPORTS_W TrackerTLD : public Tracker
BOILERPLATE_CODE("TLD",TrackerTLD);
};
/** @brief KCF is a novel tracking framework that explicitly decomposes the long-term tracking task into
tracking, learning and detection.
The tracker follows the object from frame to frame. The detector localizes all appearances that
have been observed so far and corrects the tracker if necessary. The learning estimates detector’s
errors and updates it to avoid these errors in the future. The implementation is based on @cite TLD .
The Median Flow algorithm (see cv::TrackerMedianFlow) was chosen as a tracking component in this
implementation, following authors. Tracker is supposed to be able to handle rapid motions, partial
occlusions, object absence etc.
*/
class CV_EXPORTS_W TrackerKCF : public Tracker
{
public:
struct CV_EXPORTS Params
{
Params();
void read( const FileNode& /*fn*/ );
void write( FileStorage& /*fs*/ ) const;
double sigma; // gaussian kernel bandwidth
double lambda; // regularization
double interp_factor; // linear interpolation factor for adaptation
double output_sigma_factor; // spatial bandwidth (proportional to target)
};
/** @brief Constructor
@param parameters KCF parameters TrackerKCF::Params
*/
BOILERPLATE_CODE("KCF",TrackerKCF);
};
//! @}
} /* namespace cv */

1
modules/tracking/src/tracker.cpp
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@ -110,6 +110,7 @@ Ptr<Tracker> Tracker::create( const String& trackerType )
BOILERPLATE_CODE("BOOSTING",TrackerBoosting);
BOILERPLATE_CODE("MEDIANFLOW",TrackerMedianFlow);
BOILERPLATE_CODE("TLD",TrackerTLD);
BOILERPLATE_CODE("KCF",TrackerKCF);
return Ptr<Tracker>();
}

472
modules/tracking/src/trackerKCF.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include <complex>
/*---------------------------
| TrackerKCFModel
|---------------------------*/
namespace cv{
/**
* \brief Implementation of TrackerModel for MIL algorithm
*/
class TrackerKCFModel : public TrackerModel{
public:
TrackerKCFModel(TrackerKCF::Params /*params*/){}
~TrackerKCFModel(){}
protected:
void modelEstimationImpl( const std::vector<Mat>& responses ){}
void modelUpdateImpl(){}
};
} /* namespace cv */
/*---------------------------
| TrackerKCF
|---------------------------*/
namespace cv{
/*
* Prototype
*/
class TrackerKCFImpl : public TrackerKCF{
public:
TrackerKCFImpl( const TrackerKCF::Params &parameters = TrackerKCF::Params() );
void read( const FileNode& fn );
void write( FileStorage& fs ) const;
protected:
/*
* basic functions and vars
*/
bool initImpl( const Mat& image, const Rect2d& boundingBox );
bool updateImpl( const Mat& image, Rect2d& boundingBox );
TrackerKCF::Params params;
/*
* KCF functions and vars
*/
void createHanningWindow(OutputArray _dst, cv::Size winSize, int type);
void inline fft2(Mat src, Mat & dest);
void inline ifft2(Mat src, Mat & dest);
void getSubWindow(Mat img, Rect roi, Mat& patch);
void denseGaussKernel(double sigma, Mat x, Mat y, Mat & k);
void calcResponse(Mat alphaf, Mat k, Mat & response);
void shiftRows(Mat& mat);
void shiftRows(Mat& mat,int n);
void shiftCols(Mat& mat, int n);
private:
double output_sigma;
Rect2d roi;
Mat hann; //hann window filter
Mat y,yf; // training response and its FFT
Mat x,xf; // observation and its FFT
Mat k,kf; // dense gaussian kernel and its FFT
Mat new_alphaf, alphaf; // learning rate
Mat z, new_z;
Mat response; // detection result
int frame;
};
/*
* Constructor
*/
Ptr<TrackerKCF> TrackerKCF::createTracker(const TrackerKCF::Params &parameters){
return Ptr<TrackerKCFImpl>(new TrackerKCFImpl(parameters));
}
TrackerKCFImpl::TrackerKCFImpl( const TrackerKCF::Params &parameters ) :
params( parameters )
{
isInit = false;
}
void TrackerKCFImpl::read( const cv::FileNode& fn ){
params.read( fn );
}
void TrackerKCFImpl::write( cv::FileStorage& fs ) const{
params.write( fs );
}
/*
* Initialization:
* - creating hann window filter
* - ROI padding
* - creating a gaussian response for the training ground-truth
* - perform FFT to the gaussian response
*/
bool TrackerKCFImpl::initImpl( const Mat& image, const Rect2d& boundingBox ){
frame=0;
roi = boundingBox;
//calclulate output sigma
output_sigma=sqrt(roi.width*roi.height)*params.output_sigma_factor;
output_sigma=-0.5/(output_sigma*output_sigma);
// add padding to the roi
roi.x-=roi.width/2;
roi.y-=roi.height/2+1;
roi.width*=2;
roi.height*=2;
// initialize the hann window filter
createHanningWindow(hann, roi.size(), CV_64F);
// create gaussian response
y=Mat::zeros(roi.height,roi.width,CV_64F);
for(unsigned i=0;i<roi.height;i++){
for(unsigned j=0;j<roi.width;j++){
y.at<double>(i,j)=(i-roi.height/2+1)*(i-roi.height/2+1)+(j-roi.width/2+1)*(j-roi.width/2+1);
}
}
y*=(double)output_sigma;
cv::exp(y,y);
// perform fourier transfor to the gaussian response
fft2(y,yf);
model=Ptr<TrackerKCFModel>(new TrackerKCFModel(params));
// TODO: return true only if roi inside the image
return true;
}
/*
* Main part of the KCF algorithm
*/
bool TrackerKCFImpl::updateImpl( const Mat& image, Rect2d& boundingBox ){
double minVal, maxVal; // min-max response
Point minLoc,maxLoc; // min-max location
Mat img;
// check the channels of the input image, grayscale is preferred
CV_Assert(image.channels() == 1 || image.channels() == 3);
if(image.channels()>1){
cvtColor(image,img, CV_BGR2GRAY);
}else img=image;
// extract and pre-process the patch
getSubWindow(img,roi, x);
// detection part
if(frame>0){
denseGaussKernel(params.sigma,x,z,k);
calcResponse(alphaf,k,response);
minMaxLoc( response, &minVal, &maxVal, &minLoc, &maxLoc );
roi.x+=(maxLoc.x-roi.width/2+1);roi.y+=(maxLoc.y-roi.height/2+1);
// update the bounding box
boundingBox.x=roi.x+boundingBox.width/2;
boundingBox.y=roi.y+boundingBox.height/2;
}
// extract the patch for learning purpose
getSubWindow(img,roi, x);
// Kernel Regularized Least-Squares, calculate alphas
denseGaussKernel(params.sigma,x,x,k);
fft2(k,kf);
kf=kf+params.lambda;
/* TODO: optimize this element-wise division
* new_alphaf=yf./kf
* z=[(ax+bd)+i(bc-ad)]/(c^2+d^2)
*/
new_alphaf=Mat_<Vec2d >(yf.rows, yf.cols);
std::complex<double> temp;
for(int i=0;i<yf.rows;i++){
for(int j=0;j<yf.cols;j++){
temp=std::complex<double>(yf.at<Vec2d>(i,j)[0],yf.at<Vec2d>(i,j)[1])/(std::complex<double>(kf.at<Vec2d>(i,j)[0],kf.at<Vec2d>(i,j)[1])/*+complex<float>(0.0000000001,0.0000000001)*/);
new_alphaf.at<Vec2d >(i,j)[0]=temp.real();
new_alphaf.at<Vec2d >(i,j)[1]=temp.imag();
}
}
// update the learning model
new_z=x.clone();
if(frame==0){
alphaf=new_alphaf.clone();
z=x;
}else{
alphaf=(1.0-params.interp_factor)*alphaf+params.interp_factor*new_alphaf;
z=(1.0-params.interp_factor)*z+params.interp_factor*new_z;
}
frame++;
return true;
}
/*-------------------------------------
| implementation of the KCF functions
|-------------------------------------*/
/*
* hann window filter
*/
void TrackerKCFImpl::createHanningWindow(OutputArray _dst, cv::Size winSize, int type){
CV_Assert( type == CV_32FC1 || type == CV_64FC1 );
_dst.create(winSize, type);
Mat dst = _dst.getMat();
int rows = dst.rows, cols = dst.cols;
AutoBuffer<double> _wc(cols);
double * const wc = (double *)_wc;
double coeff0 = 2.0 * CV_PI / (double)(cols - 1), coeff1 = 2.0f * CV_PI / (double)(rows - 1);
for(int j = 0; j < cols; j++)
wc[j] = 0.5 * (1.0 - cos(coeff0 * j));
if(dst.depth() == CV_32F)
{
for(int i = 0; i < rows; i++)
{
float* dstData = dst.ptr<float>(i);
double wr = 0.5 * (1.0 - cos(coeff1 * i));
for(int j = 0; j < cols; j++)
dstData[j] = (float)(wr * wc[j]);
}
}
else
{
for(int i = 0; i < rows; i++)
{
double* dstData = dst.ptr<double>(i);
double wr = 0.5 * (1.0 - cos(coeff1 * i));
for(int j = 0; j < cols; j++)
dstData[j] = wr * wc[j];
}
}
// perform batch sqrt for SSE performance gains
//cv::sqrt(dst, dst); //matlab do not use the square rooted version
}
/*
* simplification of fourier transoform function in opencv
*/
void inline TrackerKCFImpl::fft2(Mat src, Mat & dest){
Mat planes[] = {Mat_<double>(src), Mat::zeros(src.size(), CV_64F)};
merge(planes, 2, dest);
dft(dest,dest,DFT_COMPLEX_OUTPUT);
}
/*
* simplification of inverse fourier transoform function in opencv
*/
void inline TrackerKCFImpl::ifft2(Mat src, Mat & dest){
idft(src,dest,DFT_SCALE+DFT_REAL_OUTPUT);
}
/*
* obtain the patch and apply hann window filter to it
* TODO: return false if roi is outside the image, now it produce ERROR!
*/
void TrackerKCFImpl::getSubWindow(Mat img, Rect roi, Mat& patch){
Rect region=roi;
// extract patch inside the image
if(roi.x<0){region.x=0;region.width+=roi.x;}
if(roi.y<0){region.y=0;region.height+=roi.y;}
if(roi.x+roi.width>img.cols)region.width=img.cols-roi.x;
if(roi.y+roi.height>img.rows)region.height=img.rows-roi.y;
if(region.width>img.cols)region.width=img.cols;
if(region.height>img.rows)region.height=img.rows;
patch=img(region).clone();
// add some padding to compensate when the patch is outside image border
int addTop,addBottom, addLeft, addRight;
addTop=region.y-roi.y;
addBottom=(roi.height+roi.y>img.rows?roi.height+roi.y-img.rows:0);
addLeft=region.x-roi.x;
addRight=(roi.width+roi.x>img.cols?roi.width+roi.x-img.cols:0);
copyMakeBorder(patch,patch,addTop,addBottom,addLeft,addRight,BORDER_REPLICATE);
patch.convertTo(patch,CV_64F);
patch=patch/255.0-0.5; // normalize to range -0.5 .. 0.5
patch=patch.mul(hann); // hann window filter
}
/*
* dense gauss kernel function
*/
void TrackerKCFImpl::denseGaussKernel(double sigma, Mat x, Mat y, Mat & k){
Mat xf, yf, xyf,xy;
double normX, normY;
fft2(x,xf);
fft2(y,yf);
normX=norm(x);
normX*=normX;
normY=norm(y);
normY*=normY;
mulSpectrums(xf,yf,xyf,0,true);
ifft2(xyf,xyf);
shiftRows(xyf, x.rows/2);
shiftCols(xyf,x.cols/2);
//(xx + yy - 2 * xy) / numel(x)
xy=(normX+normY-2*xyf)/(x.rows*x.cols);
// TODO: check wether we really need thresholding or not
//threshold(xy,xy,0.0,0.0,THRESH_TOZERO);//max(0, (xx + yy - 2 * xy) / numel(x))
for(unsigned i=0;i<xy.rows;i++){
for(unsigned j=0;j<xy.cols;j++){
if(xy.at<double>(i,j)<0.0)xy.at<double>(i,j)=0.0;
}
}
double sig=-1.0/(sigma*sigma);
xy=sig*xy;
exp(xy,k);
}
/* CIRCULAR SHIT Function
* http://stackoverflow.com/questions/10420454/shift-like-matlab-function-rows-or-columns-of-a-matrix-in-opencv
*/
// circular shift one row from up to down
void TrackerKCFImpl::shiftRows(Mat& mat) {
Mat temp;
Mat m;
int k = (mat.rows-1);
mat.row(k).copyTo(temp);
for(; k > 0 ; k-- ) {
m = mat.row(k);
mat.row(k-1).copyTo(m);
}
m = mat.row(0);
temp.copyTo(m);
}
// circular shift n rows from up to down if n > 0, -n rows from down to up if n < 0
void TrackerKCFImpl::shiftRows(Mat& mat,int n) {
if( n < 0 ) {
n = -n;
flip(mat,mat,0);
for(int k=0; k < n;k++) {
shiftRows(mat);
}
flip(mat,mat,0);
} else {
for(int k=0; k < n;k++) {
shiftRows(mat);
}
}
}
//circular shift n columns from left to right if n > 0, -n columns from right to left if n < 0
void TrackerKCFImpl::shiftCols(Mat& mat, int n) {
if(n < 0){
n = -n;
flip(mat,mat,1);
transpose(mat,mat);
shiftRows(mat,n);
transpose(mat,mat);
flip(mat,mat,1);
} else {
transpose(mat,mat);
shiftRows(mat,n);
transpose(mat,mat);
}
}
/*
* calculate the detection response
*/
void TrackerKCFImpl::calcResponse(Mat alphaf, Mat k, Mat & response){
//alpha f--> 2channels ; k --> 1 channel;
Mat kf;
fft2(k,kf);
Mat spec;
mulSpectrums(alphaf,kf,spec,0,false);
ifft2(spec,response);
}
/*----------------------------------------------------------------------*/
/*
* Parameters
*/
TrackerKCF::Params::Params(){
sigma=0.2;
lambda=0.01;
interp_factor=0.075;
output_sigma_factor=1.0/16.0;
}
void TrackerKCF::Params::read( const cv::FileNode& fn ){
}
void TrackerKCF::Params::write( cv::FileStorage& fs ) const{
}
} /* namespace cv */
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