tracking: adding a mosse tracker

modules/tracking/doc/tracking.bib

@@ -100,3 +100,10 @@ author={Held, David and Thrun, Sebastian and Savarese, Silvio},
 booktitle = {European Conference Computer Vision (ECCV)},
 year      = {2016}
 }
+
+@inproceedings{MOSSE,
+title={Visual Object Tracking using Adaptive Correlation Filters},
+author={Bolme, David S. and Beveridge, J. Ross and Draper, Bruce A. and Lui Yui, Man},
+booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
+year      = {2010}
+}

modules/tracking/include/opencv2/tracking/tracker.hpp

@@ -1297,6 +1297,22 @@ public:
   virtual ~TrackerGOTURN() {}
 };
 
+/** @brief the MOSSE tracker
+note, that this tracker works with grayscale images, if passed bgr ones, they will get converted internally.
+@cite MOSSE Visual Object Tracking using Adaptive Correlation Filters
+*/
+
+class CV_EXPORTS_W TrackerMOSSE : public Tracker
+{
+ public:
+  /** @brief Constructor
+  */
+  CV_WRAP static Ptr<TrackerMOSSE> create();
+
+  virtual ~TrackerMOSSE() {}
+};
+
+
 /************************************ MultiTracker Class ---By Laksono Kurnianggoro---) ************************************/
 /** @brief This class is used to track multiple objects using the specified tracker algorithm.
 * The MultiTracker is naive implementation of multiple object tracking.

modules/tracking/samples/samples_utility.hpp

@@ -19,6 +19,8 @@ inline cv::Ptr<cv::Tracker> createTrackerByName(cv::String name)
         tracker = cv::TrackerMIL::create();
     else if (name == "GOTURN")
         tracker = cv::TrackerGOTURN::create();
+    else if (name == "MOSSE")
+        tracker = cv::TrackerMOSSE::create();
     else
         CV_Error(cv::Error::StsBadArg, "Invalid tracking algorithm name\n");
 

modules/tracking/samples/tracker.cpp

@@ -22,7 +22,7 @@ static void help()
        "Example of <video_name> is in opencv_extra/testdata/cv/tracking/\n"
        "Call:\n"
        "./tracker <tracker_algorithm> <video_name> <start_frame> [<bounding_frame>]\n"
-       "tracker_algorithm can be: MIL, BOOSTING, MEDIANFLOW, TLD\n"
+       "tracker_algorithm can be: MIL, BOOSTING, MEDIANFLOW, TLD, KCF, GOTURN, MOSSE.\n"
        << endl;
 
   cout << "\n\nHot keys: \n"

modules/tracking/src/mosseTracker.cpp

+// This file is part of the OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+//
+//[1] David S. Bolme et al. "Visual Object Tracking using Adaptive Correlation Filters"
+//    http://www.cs.colostate.edu/~draper/papers/bolme_cvpr10.pdf
+//
+
+//
+// credits:
+// Kun-Hsin Chen: for initial c++ code
+// Cracki: for the idea of only converting the used patch to gray
+//
+
+#include "opencv2/tracking.hpp"
+
+namespace cv {
+namespace tracking {
+
+struct DummyModel : TrackerModel
+{
+     virtual void modelUpdateImpl(){}
+     virtual void modelEstimationImpl( const std::vector<Mat>& ){}
+};
+
+
+const double eps=0.00001;      // for normalization
+const double rate=0.2;         // learning rate
+const double psrThreshold=5.7; // no detection, if PSR is smaller than this
+
+
+struct MosseImpl : TrackerMOSSE
+{
+protected:
+
+    Point2d center; //center of the bounding box
+    Size size;      //size of the bounding box
+    Mat hanWin;
+    Mat G;          //goal
+    Mat H, A, B;    //state
+
+    //  Element-wise division of complex numbers in src1 and src2
+    Mat divDFTs( const Mat &src1, const Mat &src2 ) const
+    {
+        Mat c1[2],c2[2],a1,a2,s1,s2,denom,re,im;
+
+        // split into re and im per src
+        cv::split(src1, c1);
+        cv::split(src2, c2);
+
+        // (Re2*Re2 + Im2*Im2) = denom
+        //   denom is same for both channels
+        cv::multiply(c2[0], c2[0], s1);
+        cv::multiply(c2[1], c2[1], s2);
+        cv::add(s1, s2, denom);
+
+        // (Re1*Re2 + Im1*Im1)/(Re2*Re2 + Im2*Im2) = Re
+        cv::multiply(c1[0], c2[0], a1);
+        cv::multiply(c1[1], c2[1], a2);
+        cv::divide(a1+a2, denom, re, 1.0 );
+
+        // (Im1*Re2 - Re1*Im2)/(Re2*Re2 + Im2*Im2) = Im
+        cv::multiply(c1[1], c2[0], a1);
+        cv::multiply(c1[0], c2[1], a2);
+        cv::divide(a1+a2, denom, im, -1.0);
+
+        // Merge Re and Im back into a complex matrix
+        Mat dst, chn[] = {re,im};
+        cv::merge(chn, 2, dst);
+        return dst;
+    }
+
+
+    void preProcess( Mat &window ) const
+    {
+        window.convertTo(window, CV_32F);
+        log(window + 1.0f, window);
+
+        //normalize
+        Scalar mean,StdDev;
+        meanStdDev(window, mean, StdDev);
+        window = (window-mean[0]) / (StdDev[0]+eps);
+
+        //Gaussain weighting
+        window = window.mul(hanWin);
+    }
+
+
+    double correlate( const Mat &image_sub, Point &delta_xy ) const
+    {
+        Mat IMAGE_SUB, RESPONSE, response;
+        // filter in dft space
+        dft(image_sub, IMAGE_SUB, DFT_COMPLEX_OUTPUT);
+        mulSpectrums(IMAGE_SUB, H, RESPONSE, 0, true );
+        idft(RESPONSE, response, DFT_SCALE|DFT_REAL_OUTPUT);
+        // update center position
+        double maxVal; Point maxLoc;
+        minMaxLoc(response, 0, &maxVal, 0, &maxLoc);
+        delta_xy.x = maxLoc.x - int(response.size().width/2);
+        delta_xy.y = maxLoc.y - int(response.size().height/2);
+        // normalize response
+        Scalar mean,std;
+        meanStdDev(response, mean, std);
+        return (maxVal-mean[0]) / (std[0]+eps); // PSR
+    }
+
+
+    Mat randWarp( const Mat& a ) const
+    {
+        cv::RNG rng(8031965);
+
+        // random rotation
+        double C=0.1;
+        double ang = rng.uniform(-C,C);
+        double c=cos(ang), s=sin(ang);
+        // affine warp matrix
+        Mat_<float> W(2,3);
+        W << c + rng.uniform(-C,C), -s + rng.uniform(-C,C), 0,
+             s + rng.uniform(-C,C),  c + rng.uniform(-C,C), 0;
+
+        // random translation
+        Mat_<float> center_warp(2, 1);
+        center_warp << a.cols/2, a.rows/2;
+        W.col(2) = center_warp - (W.colRange(0, 2))*center_warp;
+
+        Mat warped;
+        warpAffine(a, warped, W, a.size(), BORDER_REFLECT);
+        return warped;
+    }
+
+
+    virtual bool initImpl( const Mat& image, const Rect2d& boundingBox )
+    {
+        model = makePtr<DummyModel>();
+
+        Mat img;
+        if (image.channels() == 1)
+            img = image;
+        else
+            cvtColor(image, img, COLOR_BGR2GRAY);
+
+        int w = getOptimalDFTSize(int(boundingBox.width));
+        int h = getOptimalDFTSize(int(boundingBox.height));
+
+        //Get the center position
+        int x1 = int(floor((2*boundingBox.x+boundingBox.width-w)/2));
+        int y1 = int(floor((2*boundingBox.y+boundingBox.height-h)/2));
+        center.x = x1 + (w)/2;
+        center.y = y1 + (h)/2;
+        size.width = w;
+        size.height = h;
+
+        Mat window;
+        getRectSubPix(img, size, center, window);
+        createHanningWindow(hanWin, size, CV_32F);
+
+        // goal
+        Mat g=Mat::zeros(size,CV_32F);
+        g.at<float>(h/2, w/2) = 1;
+        GaussianBlur(g, g, Size(-1,-1), 2.0);
+        double maxVal;
+        minMaxLoc(g, 0, &maxVal);
+        g = g / maxVal;
+        dft(g, G, DFT_COMPLEX_OUTPUT);
+
+        // initial A,B and H
+        A = Mat::zeros(G.size(), G.type());
+        B = Mat::zeros(G.size(), G.type());
+        for(int i=0; i<8; i++)
+        {
+            Mat window_warp = randWarp(window);
+            preProcess(window_warp);
+
+            Mat WINDOW_WARP, A_i, B_i;
+            dft(window_warp, WINDOW_WARP, DFT_COMPLEX_OUTPUT);
+            mulSpectrums(G          , WINDOW_WARP, A_i, 0, true);
+            mulSpectrums(WINDOW_WARP, WINDOW_WARP, B_i, 0, true);
+            A+=A_i;
+            B+=B_i;
+        }
+        H = divDFTs(A,B);
+        return true;
+    }
+
+    virtual bool updateImpl( const Mat& image, Rect2d& boundingBox )
+    {
+        if (H.empty()) // not initialized
+            return false;
+
+        Mat image_sub;
+        getRectSubPix(image, size, center, image_sub);
+
+        if (image_sub.channels() != 1)
+            cvtColor(image_sub, image_sub, COLOR_BGR2GRAY);
+        preProcess(image_sub);
+
+        Point delta_xy;
+        double PSR = correlate(image_sub, delta_xy);
+        if (PSR < psrThreshold)
+            return false;
+
+        //update location
+        center.x += delta_xy.x;
+        center.y += delta_xy.y;
+
+        Mat img_sub_new;
+        getRectSubPix(image, size, center, img_sub_new);
+        if (img_sub_new.channels() != 1)
+            cvtColor(img_sub_new, img_sub_new, COLOR_BGR2GRAY);
+        preProcess(img_sub_new);
+
+        // new state for A and B
+        Mat F, A_new, B_new;
+        dft(img_sub_new, F, DFT_COMPLEX_OUTPUT);
+        mulSpectrums(G, F, A_new, 0, true );
+        mulSpectrums(F, F, B_new, 0, true );
+
+        // update A ,B, and H
+        A = A*(1-rate) + A_new*rate;
+        B = B*(1-rate) + B_new*rate;
+        H = divDFTs(A, B);
+
+        // return tracked rect
+        double x=center.x, y=center.y;
+        int w = size.width, h=size.height;
+        boundingBox = Rect2d(Point2d(x-0.5*w, y-0.5*h), Point2d(x+0.5*w, y+0.5*h));
+        return true;
+    }
+
+public:
+    MosseImpl() { isInit = 0; }
+
+    // dummy implementation.
+    virtual void read( const FileNode& ){}
+    virtual void write( FileStorage& ) const{}
+
+}; // MosseImpl
+
+} // tracking
+
+
+Ptr<TrackerMOSSE> TrackerMOSSE::create()
+{
+    return makePtr<tracking::MosseImpl>();
+}
+
+
+} // cv

modules/tracking/test/test_trackers.cpp

@@ -464,6 +464,13 @@ TEST_P(DistanceAndOverlap, DISABLED_TLD)
   TrackerTest test( TrackerTLD::create(), dataset, 60, .4f, NoTransform);
   test.run();
 }
+
+TEST_P(DistanceAndOverlap, MOSSE)
+{
+  TrackerTest test( TrackerMOSSE::create(), dataset, 22, .7f, NoTransform);
+  test.run();
+}
+
 /***************************************************************************************/
 //Tests with shifted initial window
 TEST_P(DistanceAndOverlap, Shifted_Data_MedianFlow)
@@ -495,6 +502,12 @@ TEST_P(DistanceAndOverlap, DISABLED_Shifted_Data_TLD)
   TrackerTest test( TrackerTLD::create(), dataset, 120, .2f, CenterShiftLeft);
   test.run();
 }
+
+TEST_P(DistanceAndOverlap, Shifted_Data_MOSSE)
+{
+  TrackerTest test( TrackerMOSSE::create(), dataset, 13, .69f, CenterShiftLeft);
+  test.run();
+}
 /***************************************************************************************/
 //Tests with scaled initial window
 TEST_P(DistanceAndOverlap, Scaled_Data_MedianFlow)
@@ -534,6 +547,13 @@ TEST_P(DistanceAndOverlap, DISABLED_GOTURN)
   test.run();
 }
 
+TEST_P(DistanceAndOverlap, Scaled_Data_MOSSE)
+{
+  TrackerTest test( TrackerMOSSE::create(), dataset, 22, 0.69f, Scale_1_1, 1);
+  test.run();
+}
+
+
 INSTANTIATE_TEST_CASE_P( Tracking, DistanceAndOverlap, TESTSET_NAMES);
 
 /* End of file. */