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added tutorials for KCF tracker
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‎modules/tracking/samples/tutorial_customizing_cn_tracker.cpp

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#include <opencv2/core/utility.hpp>
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#include <opencv2/tracking.hpp>
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#include <opencv2/videoio.hpp>
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#include <opencv2/highgui.hpp>
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#include <iostream>
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#include <cstring>
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using namespace std;
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using namespace cv;
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// prototype of the functino for feature extractor
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void sobelExtractor(const Mat img, const Rect roi, Mat& feat);
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int main( int argc, char** argv ){
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// show help
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if(argc<2){
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cout<<
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" Usage: tracker <video_name>\n"
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" examples:\n"
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" example_tracking_kcf Bolt/img/%04d.jpg\n"
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" example_tracking_kcf faceocc2.webm\n"
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<< endl;
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return 0;
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}
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// declares all required variables
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Rect2d roi;
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Mat frame;
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//! [param]
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TrackerKCF::Params param;
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param.desc_pca = TrackerKCF::GRAY | TrackerKCF::CN;
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param.desc_npca = 0;
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param.compress_feature = true;
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param.compressed_size = 2;
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//! [param]
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// create a tracker object
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//! [create]
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Ptr<TrackerKCF> tracker = TrackerKCF::createTracker(param);
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//! [create]
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//! [setextractor]
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tracker->setFeatureExtractor(sobelExtractor);
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//! [setextractor]
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// set input video
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std::string video = argv[1];
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VideoCapture cap(video);
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// get bounding box
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cap >> frame;
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roi=selectROI("tracker",frame);
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//quit if ROI was not selected
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if(roi.width==0 || roi.height==0)
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return 0;
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// initialize the tracker
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tracker->init(frame,roi);
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// perform the tracking process
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printf("Start the tracking process, press ESC to quit.\n");
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for ( ;; ){
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// get frame from the video
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cap >> frame;
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// stop the program if no more images
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if(frame.rows==0 || frame.cols==0)
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break;
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// update the tracking result
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tracker->update(frame,roi);
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// draw the tracked object
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rectangle( frame, roi, Scalar( 255, 0, 0 ), 2, 1 );
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// show image with the tracked object
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imshow("tracker",frame);
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//quit on ESC button
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if(waitKey(1)==27)break;
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}
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return 0;
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}
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void sobelExtractor(const Mat img, const Rect roi, Mat& feat){
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Mat sobel[2];
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Mat patch;
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Rect region=roi;
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//! [insideimage]
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// extract patch inside the image
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if(roi.x<0){region.x=0;region.width+=roi.x;}
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if(roi.y<0){region.y=0;region.height+=roi.y;}
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if(roi.x+roi.width>img.cols)region.width=img.cols-roi.x;
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if(roi.y+roi.height>img.rows)region.height=img.rows-roi.y;
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if(region.width>img.cols)region.width=img.cols;
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if(region.height>img.rows)region.height=img.rows;
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//! [insideimage]
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patch=img(region).clone();
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cvtColor(patch,patch, CV_BGR2GRAY);
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//! [padding]
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// add some padding to compensate when the patch is outside image border
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int addTop,addBottom, addLeft, addRight;
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addTop=region.y-roi.y;
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addBottom=(roi.height+roi.y>img.rows?roi.height+roi.y-img.rows:0);
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addLeft=region.x-roi.x;
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addRight=(roi.width+roi.x>img.cols?roi.width+roi.x-img.cols:0);
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copyMakeBorder(patch,patch,addTop,addBottom,addLeft,addRight,BORDER_REPLICATE);
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//! [padding]
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//! [sobel]
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Sobel(patch, sobel[0], CV_32F,1,0,1);
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Sobel(patch, sobel[1], CV_32F,0,1,1);
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merge(sobel,2,feat);
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//! [sobel]
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//! [postprocess]
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feat.convertTo(feat,CV_64F);
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feat=feat/255.0-0.5; // normalize to range -0.5 .. 0.5
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//! [postprocess]
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}

‎modules/tracking/samples/tutorial_introduction_to_tracker.cpp

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#include <opencv2/core/utility.hpp>
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#include <opencv2/tracking.hpp>
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#include <opencv2/videoio.hpp>
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#include <opencv2/highgui.hpp>
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#include <iostream>
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#include <cstring>
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using namespace std;
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using namespace cv;
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int main( int argc, char** argv ){
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// show help
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//! [help]
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if(argc<2){
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cout<<
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" Usage: tracker <video_name>\n"
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" examples:\n"
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" example_tracking_kcf Bolt/img/%04d.jpg\n"
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" example_tracking_kcf faceocc2.webm\n"
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<< endl;
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return 0;
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}
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//! [help]
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// declares all required variables
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//! [vars]
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Rect2d roi;
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Mat frame;
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//! [vars]
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// create a tracker object
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//! [create]
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Ptr<Tracker> tracker = Tracker::create( "KCF" );
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//! [create]
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// set input video
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//! [setvideo]
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std::string video = argv[1];
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VideoCapture cap(video);
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//! [setvideo]
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// get bounding box
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//! [getframe]
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cap >> frame;
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//! [getframe]
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//! [selectroi]
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roi=selectROI("tracker",frame);
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//! [selectroi]
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//quit if ROI was not selected
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if(roi.width==0 || roi.height==0)
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return 0;
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// initialize the tracker
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//! [init]
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tracker->init(frame,roi);
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//! [init]
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// perform the tracking process
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printf("Start the tracking process, press ESC to quit.\n");
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for ( ;; ){
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// get frame from the video
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cap >> frame;
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// stop the program if no more images
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if(frame.rows==0 || frame.cols==0)
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break;
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// update the tracking result
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//! [update]
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tracker->update(frame,roi);
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//! [update]
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//! [visualization]
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// draw the tracked object
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rectangle( frame, roi, Scalar( 255, 0, 0 ), 2, 1 );
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// show image with the tracked object
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imshow("tracker",frame);
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//! [visualization]
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//quit on ESC button
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if(waitKey(1)==27)break;
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}
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return 0;
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}

‎modules/tracking/samples/tutorial_multitracker.cpp

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/*----------------------------------------------
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* Usage:
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* example_tracking_multitracker <video_name> [algorithm]
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*
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* example:
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* example_tracking_multitracker Bolt/img/%04d.jpg
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* example_tracking_multitracker faceocc2.webm KCF
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*--------------------------------------------------*/
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#include <opencv2/core/utility.hpp>
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#include <opencv2/tracking.hpp>
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#include <opencv2/videoio.hpp>
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#include <opencv2/highgui.hpp>
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#include <iostream>
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#include <cstring>
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#include <ctime>
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using namespace std;
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using namespace cv;
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int main( int argc, char** argv ){
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// show help
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if(argc<2){
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cout<<
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" Usage: example_tracking_multitracker <video_name> [algorithm]\n"
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" examples:\n"
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" example_tracking_multitracker Bolt/img/%04d.jpg\n"
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" example_tracking_multitracker faceocc2.webm MEDIANFLOW\n"
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<< endl;
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return 0;
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}
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// set the default tracking algorithm
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std::string trackingAlg = "KCF";
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// set the tracking algorithm from parameter
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if(argc>2)
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trackingAlg = argv[2];
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// create the tracker
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//! [create]
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MultiTracker trackers(trackingAlg);
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//! [create]
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// container of the tracked objects
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//! [roi]
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vector<Rect2d> objects;
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//! [roi]
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// set input video
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std::string video = argv[1];
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VideoCapture cap(video);
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Mat frame;
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// get bounding box
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cap >> frame;
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//! [selectmulti]
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selectROI("tracker",frame,objects);
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//! [selectmulti]
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//quit when the tracked object(s) is not provided
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if(objects.size()<1)
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return 0;
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// initialize the tracker
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//! [init]
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trackers.add(frame,objects);
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//! [init]
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// do the tracking
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printf("Start the tracking process, press ESC to quit.\n");
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for ( ;; ){
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// get frame from the video
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cap >> frame;
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// stop the program if no more images
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if(frame.rows==0 || frame.cols==0)
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break;
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//update the tracking result
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//! [update]
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trackers.update(frame);
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//! [update]
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//! [result]
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// draw the tracked object
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for(unsigned i=0;i<trackers.objects.size();i++)
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rectangle( frame, trackers.objects[i], Scalar( 255, 0, 0 ), 2, 1 );
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//! [result]
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// show image with the tracked object
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imshow("tracker",frame);
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//quit on ESC button
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if(waitKey(1)==27)break;
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}
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}

‎modules/tracking/tutorials/tutorial_customizing_cn_tracker.markdown

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Customizing the CN Tracker {#tutorial_customizing_cn_tracker}
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======================
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Goal
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----
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In this tutorial you will learn how to
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- Set custom parameters for CN tracker.
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- Use your own feature-extractor function for the CN tracker.
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This document contains tutorial for the @ref cv::TrackerKCF.
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Source Code
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-----------
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@includelineno tracking/samples/tutorial_customizing_cn_tracker.cpp
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Explanation
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-----------
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This part explains how to set custom parameters and use your own feature-extractor function for the CN tracker.
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If you need a more detailed information to use @ref cv::Tracker, please refer to @ref tutorial_introduction_to_tracker.
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-# **Set Custom Parameters**
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@snippet tracking/samples/tutorial_customizing_cn_tracker.cpp param
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To set custom paramters, an object should be created. Each tracker algorithm has their own parameter format.
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So, in this case we should use parameter from @ref cv::TrackerKCF since we are interested in modifying the parameter of this tracker algorithm.
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There are several parameters that can be configured as explained in @ref cv::TrackerKCF::Params.
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For this tutorial, we focussed on the feature extractor functions.
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Several feature types can be used in @ref cv::TrackerKCF.
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In this case, the grayscale value (1 dimension) and color-names features (10 dimension),
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will be merged as 11 dimension feature and then compressed into 2 dimension as specified in the code.
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If you want to use another type of pre-defined feature-extractor function, you can check in @ref cv::TrackerKCF::MODE.
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We will leave the non-compressed feature as 0 since we want to use a customized function.
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-# **Using a custom function**
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You can define your own feature-extractor function for the CN tracker.
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However, you need to take care about several things:
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- The extracted feature should have the same size as the size of the given bounding box (width and height).
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For the number of channels you can check the limitation in @ref cv::Mat.
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- You can only use features that can be compared using Euclidean distance.
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Features like local binary pattern (LBP) may not be suitable since it should be compared using Hamming distance.
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Since the size of the extracted feature should be in the same size with the given bounding box,
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we need to take care whenever the given bounding box is partially out of range.
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In this case, we can copy part of image contained in the bounding box as shown in the snippet below.
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@snippet tracking/samples/tutorial_customizing_cn_tracker.cpp insideimage
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Whenever the copied image is smaller than the given bounding box,
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padding should be given to the sides where the bounding box is partially out of frame.
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@snippet tracking/samples/tutorial_customizing_cn_tracker.cpp padding
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-# **Defining the feature**
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In this tutorial, the extracted feature is reponse of the Sobel filter in x and y direction.
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Those Sobel filter responses are concatenated, resulting a feature with 2 channels.
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@snippet tracking/samples/tutorial_customizing_cn_tracker.cpp sobel
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-# **Post processing**
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Make sure to convert the feature into @ref cv::CV_64F data format and normalize its value with range -0.5 to 0.5
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@snippet tracking/samples/tutorial_customizing_cn_tracker.cpp postprocess

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