opencv：多目标跟踪

本文转自：http://stackoverflow.com/questions/25494595/multiple-object-tracking-with-kalman-filter。

代码流程：

   1 Background Subtract
    2 Smoothing , Blur etc. filters.
    3 Find Contours
    4 Draw Rectangle and find Centroid.
    5 Apply Kalman Filter

#include 
#include 
//#include 
using namespace std;
using namespace cv;

#define drawCross( img, center, color, d )\
line(img, Point(center.x - d, center.y - d), Point(center.x + d, center.y + d), color, 2, CV_AA, 0);\
line(img, Point(center.x + d, center.y - d), Point(center.x - d, center.y + d), color, 2, CV_AA, 0 )\

vector mousev,kalmanv;


cv::KalmanFilter KF;
cv::Mat_ measurement(2,1); 
Mat_ state(4, 1); // (x, y, Vx, Vy)
int incr=0;


void initKalman(float x, float y)
{
    // Instantate Kalman Filter with
    // 4 dynamic parameters and 2 measurement parameters,
    // where my measurement is: 2D location of object,
    // and dynamic is: 2D location and 2D velocity.
    KF.init(4, 2, 0);

    measurement = Mat_::zeros(2,1);
    measurement.at(0, 0) = x;
    measurement.at(0, 0) = y;


    KF.statePre.setTo(0);
    KF.statePre.at(0, 0) = x;
    KF.statePre.at(1, 0) = y;

    KF.statePost.setTo(0);
    KF.statePost.at(0, 0) = x;
    KF.statePost.at(1, 0) = y; 

    setIdentity(KF.transitionMatrix);
    setIdentity(KF.measurementMatrix);
    setIdentity(KF.processNoiseCov, Scalar::all(.005)); //adjust this for faster convergence - but higher noise
    setIdentity(KF.measurementNoiseCov, Scalar::all(1e-1));
    setIdentity(KF.errorCovPost, Scalar::all(.1));
}

Point kalmanPredict() 
{
    Mat prediction = KF.predict();
    Point predictPt(prediction.at(0),prediction.at(1));

    KF.statePre.copyTo(KF.statePost);
    KF.errorCovPre.copyTo(KF.errorCovPost);

    return predictPt;
}

Point kalmanCorrect(float x, float y)
{
    measurement(0) = x;
    measurement(1) = y;
    Mat estimated = KF.correct(measurement);
    Point statePt(estimated.at(0),estimated.at(1));
    return statePt;
}

int main()
{
  Mat frame, thresh_frame;
  vector channels;
  VideoCapture capture;
  vector hierarchy;
  vector > contours;

   // cv::Mat frame;
    cv::Mat back;
    cv::Mat fore;
    cv::BackgroundSubtractorMOG2 bg;

    //bg.nmixtures = 3;//nmixtures
    //bg.bShadowDetection = false;
    int incr=0;
    int track=0;

    capture.open("4.avi");

  if(!capture.isOpened())
    cerr << "Problem opening video source" << endl;


  mousev.clear();
  kalmanv.clear();

initKalman(0, 0);

  while((char)waitKey(1) != 'q' && capture.grab())
    {

   Point s, p;

  capture.retrieve(frame);

        bg.operator ()(frame,fore);
        bg.getBackgroundImage(back);
        erode(fore,fore,Mat());
        erode(fore,fore,Mat());
        dilate(fore,fore,Mat());
        dilate(fore,fore,Mat());
        dilate(fore,fore,Mat());
        dilate(fore,fore,Mat());
        dilate(fore,fore,Mat());
        dilate(fore,fore,Mat());
        dilate(fore,fore,Mat());

        cv::normalize(fore, fore, 0, 1., cv::NORM_MINMAX);
        cv::threshold(fore, fore, .5, 1., CV_THRESH_BINARY);


      split(frame, channels);
      add(channels[0], channels[1], channels[1]);
      subtract(channels[2], channels[1], channels[2]);
      threshold(channels[2], thresh_frame, 50, 255, CV_THRESH_BINARY);
      medianBlur(thresh_frame, thresh_frame, 5);

//       imshow("Red", channels[1]);
      findContours(fore, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
      vector > contours_poly( contours.size() );
      vector boundRect( contours.size() );

      Mat drawing = Mat::zeros(thresh_frame.size(), CV_8UC1);
      for(size_t i = 0; i < contours.size(); i++)
        {
//          cout << contourArea(contours[i]) << endl;
          if(contourArea(contours[i]) > 500)
            drawContours(drawing, contours, i, Scalar::all(255), CV_FILLED, 8, vector(), 0, Point());
        }
      thresh_frame = drawing;

      findContours(fore, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));

      drawing = Mat::zeros(thresh_frame.size(), CV_8UC1);
      for(size_t i = 0; i < contours.size(); i++)
        {
//          cout << contourArea(contours[i]) << endl;
          if(contourArea(contours[i]) > 3000)
            drawContours(drawing, contours, i, Scalar::all(255), CV_FILLED, 8, vector(), 0, Point());
      }
      thresh_frame = drawing;

// Get the moments
      vector mu(contours.size() );
      for( size_t i = 0; i < contours.size(); i++ )
      { 
          mu[i] = moments( contours[i], false ); }

//  Get the mass centers:
      vector mc( contours.size() );
      for( size_t i = 0; i < contours.size(); i++ ) 

      { 
          mc[i] = Point2f( mu[i].m10/mu[i].m00 , mu[i].m01/mu[i].m00 ); 


     /*  
      for(size_t i = 0; i < mc.size(); i++)
        {

     //       drawCross(frame, mc[i], Scalar(255, 0, 0), 5);
          //measurement(0) = mc[i].x;
          //measurement(1) = mc[i].y;


//        line(frame, p, s, Scalar(255,255,0), 1);

//          if (measurement(1) <= 130 && measurement(1) >= 120) {
  //            incr++;          
    //         cout << "Conter " << incr << " Loation " << measurement(1) << endl;
      //   }
      }*/
      }


        for( size_t i = 0; i < contours.size(); i++ )
       { approxPolyDP( Mat(contours[i]), contours_poly[i], 3, true );
         boundRect[i] = boundingRect( Mat(contours_poly[i]) );

     }

              p = kalmanPredict();
//        cout << "kalman prediction: " << p.x << " " << p.y << endl;
          mousev.push_back(p);

      for( size_t i = 0; i < contours.size(); i++ )
       {
           if(contourArea(contours[i]) > 1000){
         rectangle( frame, boundRect[i].tl(), boundRect[i].br(), Scalar(0, 255, 0), 2, 8, 0 );
        Point center = Point(boundRect[i].x + (boundRect[i].width /2), boundRect[i].y + (boundRect[i].height/2));
        cv::circle(frame,center, 8, Scalar(0, 0, 255), -1, 1,0);



         s = kalmanCorrect(center.x, center.y);
        drawCross(frame, s, Scalar(255, 255, 255), 5);

        if (s.y <= 130 && p.y > 130 && s.x > 15) {
            incr++;
             cout << "Counter " << incr << endl;
           }


             for (int i = mousev.size()-20; i < mousev.size()-1; i++) {
                 line(frame, mousev[i], mousev[i+1], Scalar(0,255,0), 1);
                 }

              }
       }


      imshow("Video", frame);
      imshow("Red", channels[2]);
      imshow("Binary", thresh_frame);
    }
  return 0;
}