多目标跟踪计数opencv（C++实现）

今天小博就把多目标检测，追踪，计数的代码贴出来供大家把玩，如有有疑问，可发邮件到1039463596@qq.com，欢迎提出批评指正：

我们先看一下追踪计数的效果吧



1． 算法目的：

运动目标跟踪算法的目的就是对视频中的图象序列进行分析，计算出目标在每帧图象上的位置。这里要根据区域分割过程给出的目标质心位置，计算出目标位移，并且根据质心位置的变化判断出目标的运动方向，以及运动目标是否在观察窗口，实现对客流量的统计。因为该跟踪是对多目标的追踪，需要找出运动目标在相邻帧上的对应区域。

系统具有固有噪声，目标周围背景的干扰可能会产生误差，但这些噪声在前面的过程已经去除，如有必要可做适当调整修正。

2． 算法难点：

（1）因为要跟踪的是多目标，需要找到相邻帧之间对应的运动目标区域不致跟踪混乱。

（2）如何判断运动目标区域是否是新的目标进入观测窗口

（3）运动目标是否离开了观测窗口以及离开的方向；即计数器何时加1、是否加1

（4）对跟踪过程中出现的一些偏差和问题，要进行必要的修正

3． 算法描述：

（1）跟踪首先要判断的是：帧与帧之间如何将运动目标对应起来。追踪过程中的追踪特

征是物体的质心（由运动区域分割过程中给出），这里判断对应目标可以：a.只利用质心间的最短距离做为特征; b.利用加权系数将最短距离，运动目标区域的长度，宽度以及长宽比和面积等综合起来作为特征。

（2）根据判断特征设置目标链，记录每个被跟踪目标的最新质心位置，为下步判断提供条件。另外将每个目标的质心位置存储起来，可以随时掌握目标的运动情况，为以后要输出目标的运动曲线做基础。

（3）每进入观察窗口一个新的运动目标，就将它的最新质心位置加入该目标链。如何判断该运动目标是新的：设置门限值ymin,ymax（当ymin

#include <iostream>
#include <opencv2/opencv.hpp>
//#include <opencv2/video/background_segm.hpp>
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<Point> mousev,kalmanv;

cv::KalmanFilter KF;
cv::Mat_<float> measurement(2,1);
Mat_<float> 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_<float>::zeros(2,1);
measurement.at<float>(0, 0) = x;
measurement.at<float>(0, 0) = y;

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

KF.statePost.setTo(0);
KF.statePost.at<float>(0, 0) = x;
KF.statePost.at<float>(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<float>(0),prediction.at<float>(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<float>(0),estimated.at<float>(1));
return statePt;
}

int main()
{
Mat frame, thresh_frame;
vector<Mat> channels;
VideoCapture capture;
vector<Vec4i> hierarchy;
vector<vector<Point> > 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<vector<Point> > contours_poly( contours.size() );
vector<Rect> 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<Vec4i>(), 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<Vec4i>(), 0, Point());
}
thresh_frame = drawing;

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

//  Get the mass centers:
vector<Point2f> 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;
}

运动目标检测与卡尔曼滤波追踪结合的追踪方法实现了这一过程。。。`