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modules/tracking/doc/tracking.bib
View file @ 660174e8
......@@ -83,13 +83,13 @@
year = {2012},
}
@INPROCEEDINGS{KCF_CN,
author={Danelljan, M. and Khan, F.S. and Felsberg, M. and van de Weijer, J.},
booktitle={Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on},
title={Adaptive Color Attributes for Real-Time Visual Tracking},
year={2014},
month={June},
pages={1090-1097},
keywords={computer vision;feature extraction;image colour analysis;image representation;image sequences;adaptive color attributes;benchmark color sequences;color features;color representations;computer vision;image description;real-time visual tracking;tracking-by-detection framework;Color;Computational modeling;Covariance matrices;Image color analysis;Kernel;Target tracking;Visualization;Adaptive Dimensionality Reduction;Appearance Model;Color Features;Visual Tracking},
@INPROCEEDINGS{KCF_CN,
author={Danelljan, M. and Khan, F.S. and Felsberg, M. and van de Weijer, J.},
booktitle={Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on},
title={Adaptive Color Attributes for Real-Time Visual Tracking},
year={2014},
month={June},
pages={1090-1097},
keywords={computer vision;feature extraction;image colour analysis;image representation;image sequences;adaptive color attributes;benchmark color sequences;color features;color representations;computer vision;image description;real-time visual tracking;tracking-by-detection framework;Color;Computational modeling;Covariance matrices;Image color analysis;Kernel;Target tracking;Visualization;Adaptive Dimensionality Reduction;Appearance Model;Color Features;Visual Tracking},
doi={10.1109/CVPR.2014.143},
}
modules/tracking/include/opencv2/tracking/tracker.hpp
View file @ 660174e8
......@@ -1191,8 +1191,8 @@ class CV_EXPORTS_W TrackerTLD : public Tracker
/** @brief KCF is a novel tracking framework that utilizes properties of circulant matrix to enhance the processing speed.
* This tracking method is an implementation of @cite KCF_ECCV which is extended to KFC with color-names features (@cite KCF_CN).
* The original paper of KCF is available at <http://home.isr.uc.pt/~henriques/circulant/index.html>
* as well as the matlab implementation. For more information about KCF with color-names features, please refer to
* The original paper of KCF is available at <http://home.isr.uc.pt/~henriques/circulant/index.html>
* as well as the matlab implementation. For more information about KCF with color-names features, please refer to
* <http://www.cvl.isy.liu.se/research/objrec/visualtracking/colvistrack/index.html>.
*/
class CV_EXPORTS_W TrackerKCF : public Tracker
......@@ -1202,21 +1202,21 @@ class CV_EXPORTS_W TrackerKCF : public Tracker
struct CV_EXPORTS Params
{
Params();
/**
* \brief Read parameters from file, currently unused
*/
void read( const FileNode& /*fn*/ );
/**
* \brief Read parameters from file, currently unused
*/
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)
double output_sigma_factor; //!< spatial bandwidth (proportional to target)
double pca_learning_rate; //!< compression learning rate
bool resize; //!< activate the resize feature to improve the processing speed
bool splitCoeff; //!< split the training coefficients into two matrices
......
modules/tracking/src/trackerKCF.cpp
View file @ 660174e8
......@@ -65,7 +65,7 @@ namespace cv{
| TrackerKCF
|---------------------------*/
namespace cv{
/*
* Prototype
*/
......@@ -74,23 +74,23 @@ namespace cv{
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, const cv::Size winSize, const int type) const;
void inline fft2(const Mat src, std::vector<Mat> & dest) const;
void inline fft2(const Mat src, Mat & dest) const;
void inline ifft2(const Mat src, Mat & dest) const ;
void inline ifft2(const Mat src, Mat & dest) const;
void inline pixelWiseMult(const std::vector<Mat> src1, const std::vector<Mat> src2, std::vector<Mat> & dest, const int flags, const bool conjB=false) const;
void inline sumChannels(std::vector<Mat> src, Mat & dest) const;
void inline updateProjectionMatrix(const Mat src, Mat & old_cov,Mat & _proj_mtx,double pca_rate, int compressed_sz) const;
......@@ -100,16 +100,16 @@ namespace cv{
void denseGaussKernel(const double sigma, const Mat _x, const Mat _y, Mat & _k) const;
void calcResponse(const Mat _alphaf, const Mat _k, Mat & _response) const;
void calcResponse(const Mat _alphaf, const Mat _alphaf_den, const Mat _k, Mat & _response) const;
void shiftRows(Mat& mat) const;
void shiftRows(Mat& mat) const;
void shiftRows(Mat& mat, int n) const;
void shiftCols(Mat& mat, int n) const;
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
......@@ -119,12 +119,12 @@ namespace cv{
Mat z, new_z; // model
Mat response; // detection result
Mat old_cov_mtx, proj_mtx; // for feature compression
bool resizeImage; // resize the image whenever needed and the patch size is large
int frame;
};
/*
* Constructor
*/
......@@ -136,10 +136,10 @@ namespace cv{
{
isInit = false;
resizeImage = false;
CV_Assert(params.descriptor == GRAY || params.descriptor == CN /*|| params.descriptor == CN2*/);
}
void TrackerKCFImpl::read( const cv::FileNode& fn ){
params.read( fn );
}
......@@ -147,9 +147,9 @@ namespace cv{
void TrackerKCFImpl::write( cv::FileStorage& fs ) const {
params.write( fs );
}
/*
* Initialization:
* Initialization:
* - creating hann window filter
* - ROI padding
* - creating a gaussian response for the training ground-truth
......@@ -158,11 +158,11 @@ namespace cv{
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);
//resize the ROI whenever needed
if(params.resize && roi.width*roi.height>params.max_patch_size){
resizeImage=true;
......@@ -170,21 +170,21 @@ namespace cv{
roi.y/=2.0;
roi.width/=2.0;
roi.height/=2.0;
}
}
// add padding to the roi
roi.x-=roi.width/2;
roi.y-=roi.height/2;
roi.width*=2;
roi.height*=2;
// initialize the hann window filter
createHanningWindow(hann, roi.size(), CV_64F);
if(params.descriptor==CN){
Mat layers[] = {hann, hann, hann, hann, hann, hann, hann, hann, hann, hann};
merge(layers, 10, hann);
merge(layers, 10, hann);
}
// create gaussian response
y=Mat::zeros((int)roi.height,(int)roi.width,CV_64F);
for(unsigned i=0;i<roi.height;i++){
......@@ -192,19 +192,19 @@ namespace cv{
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
*/
......@@ -212,17 +212,17 @@ namespace cv{
double minVal, maxVal; // min-max response
Point minLoc,maxLoc; // min-max location
Mat zc;
Mat img=image.clone();
// check the channels of the input image, grayscale is preferred
CV_Assert(image.channels() == 1 || image.channels() == 3);
// resize the image whenever needed
if(resizeImage)resize(img,img,Size(img.cols/2,img.rows/2));
// extract and pre-process the patch
if(!getSubWindow(img,roi, x))return false;
// detection part
if(frame>0){
//compute the gaussian kernel
......@@ -232,52 +232,52 @@ namespace cv{
denseGaussKernel(params.sigma,x,zc,k);
}else
denseGaussKernel(params.sigma,x,z,k);
// calculate filter response
if(params.splitCoeff)
calcResponse(alphaf,alphaf_den,k,response);
else
calcResponse(alphaf,k,response);
// extract the maximum 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=(resizeImage?roi.x*2:roi.x)+boundingBox.width/2;
boundingBox.y=(resizeImage?roi.y*2:roi.y)+boundingBox.height/2;
}
// extract the patch for learning purpose
if(!getSubWindow(img,roi, x))return false;
//update the training data
new_z=x.clone();
if(frame==0)
z=x.clone();
else
z=(1.0-params.interp_factor)*z+params.interp_factor*new_z;
z=(1.0-params.interp_factor)*z+params.interp_factor*new_z;
if(params.compressFeature){
// feature compression
updateProjectionMatrix(z,old_cov_mtx,proj_mtx,params.pca_learning_rate,params.compressed_size);
compress(proj_mtx,x,x);
}
// Kernel Regularized Least-Squares, calculate alphas
denseGaussKernel(params.sigma,x,x,k);
fft2(k,kf);
kf_lambda=kf+params.lambda;
/* TODO: optimize this element-wise division
* new_alphaf=yf./kf
* z=(a+bi)/(c+di)[(ac+bd)+i(bc-ad)]/(c^2+d^2)
*/
new_alphaf=Mat_<Vec2d >(yf.rows, yf.cols);
*/
new_alphaf=Mat_<Vec2d >(yf.rows, yf.cols);
std::complex<double> temp;
if(params.splitCoeff){
mulSpectrums(yf,kf,new_alphaf,0);
mulSpectrums(kf,kf_lambda,new_alphaf_den,0);
......@@ -290,7 +290,7 @@ namespace cv{
}
}
}
// update the RLS model
if(frame==0){
alphaf=new_alphaf.clone();
......@@ -299,17 +299,17 @@ namespace cv{
alphaf=(1.0-params.interp_factor)*alphaf+params.interp_factor*new_alphaf;
if(params.splitCoeff)alphaf_den=(1.0-params.interp_factor)*alphaf_den+params.interp_factor*new_alphaf_den;
}
frame++;
return true;
}
/*-------------------------------------
| implementation of the KCF functions
|-------------------------------------*/
/*
/*
* hann window filter
*/
void TrackerKCFImpl::createHanningWindow(OutputArray _dst, const cv::Size winSize, const int type) const {
......@@ -346,30 +346,30 @@ namespace cv{
// perform batch sqrt for SSE performance gains
//cv::sqrt(dst, dst); //matlab do not use the square rooted version
}
/*
* simplification of fourier transform function in opencv
*/
void inline TrackerKCFImpl::fft2(const Mat src, Mat & dest) const {
std::vector<Mat> layers(src.channels());
std::vector<Mat> outputs(src.channels());
split(src, layers);
for(int i=0;i<src.channels();i++){
dft(layers[i],outputs[i],DFT_COMPLEX_OUTPUT);
}
merge(outputs,dest);
}
void inline TrackerKCFImpl::fft2(const Mat src, std::vector<Mat> & dest) const {
std::vector<Mat> layers(src.channels());
dest.clear();
dest.resize(src.channels());
split(src, layers);
for(int i=0;i<src.channels();i++){
dft(layers[i],dest[i],DFT_COMPLEX_OUTPUT);
}
......@@ -381,19 +381,19 @@ namespace cv{
void inline TrackerKCFImpl::ifft2(const Mat src, Mat & dest) const {
idft(src,dest,DFT_SCALE+DFT_REAL_OUTPUT);
}
/*
* Point-wise multiplication of two Multichannel Mat data
*/
void inline TrackerKCFImpl::pixelWiseMult(const std::vector<Mat> src1, const std::vector<Mat> src2, std::vector<Mat> & dest, const int flags, const bool conjB) const {
dest.clear();
dest.resize(src1.size());
for(unsigned i=0;i<src1.size();i++){
mulSpectrums(src1[i], src2[i], dest[i],flags,conjB);
}
}
/*
* Combines all channels in a multi-channels Mat data into a single channel
*/
......@@ -403,18 +403,18 @@ namespace cv{
dest+=src[i];
}
}
/*
* obtains the projection matrix using PCA
*/
void inline TrackerKCFImpl::updateProjectionMatrix(const Mat src, Mat & old_cov,Mat & _proj_mtx, double pca_rate, int compressed_sz) const {
CV_Assert(compressed_sz<=src.channels());
// compute average
std::vector<Mat> layers(src.channels());
std::vector<Scalar> average(src.channels());
split(src,layers);
for (int i=0;i<src.channels();i++){
average[i]=mean(layers[i]);
layers[i]-=average[i];
......@@ -424,25 +424,25 @@ namespace cv{
Mat data,new_cov;
merge(layers,data);
data=data.reshape(1,src.rows*src.cols);
new_cov=1.0/(double)(src.rows*src.cols-1)*(data.t()*data);
if(old_cov.rows==0)old_cov=new_cov.clone();
// calc PCA
Mat w, u, vt;
SVD::compute((1.0-pca_rate)*old_cov+pca_rate*new_cov, w, u, vt);
// extract the projection matrix
_proj_mtx=u(Rect(0,0,compressed_sz,src.channels())).clone();
_proj_mtx=u(Rect(0,0,compressed_sz,src.channels())).clone();
Mat proj_vars=Mat::eye(compressed_sz,compressed_sz,_proj_mtx.type());
for(int i=0;i<compressed_sz;i++){
proj_vars.at<double>(i,i)=w.at<double>(i);
}
}
// update the covariance matrix
old_cov=(1.0-pca_rate)*old_cov+pca_rate*_proj_mtx*proj_vars*_proj_mtx.t();
}
/*
* compress the features
*/
......@@ -451,22 +451,22 @@ namespace cv{
Mat compressed=data*_proj_mtx;
dest=compressed.reshape(_proj_mtx.cols,src.rows).clone();
}
/*
* obtain the patch and apply hann window filter to it
*/
bool TrackerKCFImpl::getSubWindow(const Mat img, const Rect _roi, Mat& patch) const {
Rect region=_roi;
// return false if roi is outside the image
if((_roi.x+_roi.width<0)
||(_roi.y+_roi.height<0)
||(_roi.x>=img.cols)
||(_roi.y>=img.rows)
)return false;
// extract patch inside the image
// 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;
......@@ -475,7 +475,7 @@ namespace cv{
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;
......@@ -485,7 +485,7 @@ namespace cv{
copyMakeBorder(patch,patch,addTop,addBottom,addLeft,addRight,BORDER_REPLICATE);
if(patch.rows==0 || patch.cols==0)return false;
// extract the desired descriptors
switch(params.descriptor){
case GRAY:
......@@ -501,21 +501,21 @@ namespace cv{
if(patch.channels()>1)cvtColor(patch,patch, CV_BGR2GRAY);
break;
}
patch=patch.mul(hann); // hann window filter
return true;
}
/* Convert BGR to ColorNames
*/
void TrackerKCFImpl::extractCN(Mat _patch, Mat & cnFeatures) const {
Vec3b & pixel = _patch.at<Vec3b>(0,0);
unsigned index;
Mat temp = Mat::zeros(_patch.rows,_patch.cols,CV_64FC(10));
for(int i=0;i<_patch.rows;i++){
for(int j=0;j<_patch.cols;j++){
pixel=_patch.at<Vec3b>(i,j);
......@@ -527,10 +527,10 @@ namespace cv{
}
}
}
cnFeatures=temp.clone();
}
/*
* dense gauss kernel function
*/
......@@ -538,24 +538,24 @@ namespace cv{
std::vector<Mat> _xf,_yf,xyf_v;
Mat xy,xyf;
double normX, normY;
fft2(_x,_xf);
fft2(_y,_yf);
normX=norm(_x);
normX*=normX;
normY=norm(_y);
normY*=normY;
pixelWiseMult(_xf,_yf,xyf_v,0,true);
sumChannels(xyf_v,xyf);
ifft2(xyf,xyf);
if(params.wrapKernel){
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*_x.channels());
......@@ -566,13 +566,13 @@ namespace cv{
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 SHIFT Function
* http://stackoverflow.com/questions/10420454/shift-like-matlab-function-rows-or-columns-of-a-matrix-in-opencv
*/
......@@ -601,7 +601,7 @@ namespace cv{
shiftRows(mat);
}
flip(mat,mat,0);
}else{
}else{
for(int _k=0; _k < n;_k++) {
shiftRows(mat);
}
......@@ -628,14 +628,14 @@ namespace cv{
* calculate the detection response
*/
void TrackerKCFImpl::calcResponse(const Mat _alphaf, const Mat _k, Mat & _response) const {
//alpha f--> 2channels ; k --> 1 channel;
//alpha f--> 2channels ; k --> 1 channel;
Mat _kf;
fft2(_k,_kf);
Mat spec;
mulSpectrums(_alphaf,_kf,spec,0,false);
ifft2(spec,_response);
}
/*
* calculate the detection response for splitted form
*/
......@@ -643,11 +643,11 @@ namespace cv{
Mat _kf;
fft2(_k,_kf);
Mat spec;
Mat spec2=Mat_<Vec2d >(_k.rows, _k.cols);
Mat spec2=Mat_<Vec2d >(_k.rows, _k.cols);
std::complex<double> temp;
mulSpectrums(_alphaf,_kf,spec,0,false);
for(int i=0;i<_k.rows;i++){
for(int j=0;j<_k.cols;j++){
temp=std::complex<double>(spec.at<Vec2d>(i,j)[0],spec.at<Vec2d>(i,j)[1])/(std::complex<double>(_alphaf_den.at<Vec2d>(i,j)[0],_alphaf_den.at<Vec2d>(i,j)[1])/*+std::complex<double>(0.0000000001,0.0000000001)*/);
......@@ -655,11 +655,11 @@ namespace cv{
spec2.at<Vec2d >(i,j)[1]=temp.imag();
}
}
ifft2(spec2,_response);
}
/*----------------------------------------------------------------------*/
/*
* Parameters
*/
......@@ -673,7 +673,7 @@ namespace cv{
descriptor=CN;
splitCoeff=true;
wrapKernel=false;
//feature compression
compressFeature=true;
compressed_size=2;
......@@ -683,5 +683,5 @@ namespace cv{
void TrackerKCF::Params::read( const cv::FileNode& /*fn*/ ){}
void TrackerKCF::Params::write( cv::FileStorage& /*fs*/ ) const{}
} /* namespace cv */
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