我是OpenCV的新手。我一直希望学习新的图像处理技术/编程知识。我已经看了一些关于目标检测、跟踪、计数等方面的教程。我希望能够学习这些知识，并尝试制作自己类似的项目。
在互联网和论文中进行了大量搜索后，我最终了解到了用于目标跟踪的卡尔曼滤波器。根据以下代码，我使用了以下方法：
- 背景减法
- 平滑、模糊等滤波器。
- 查找轮廓
- 绘制矩形并查找质心。
- 应用卡尔曼滤波器
现在，我可以使用我的代码跟踪一个对象。我想要跟踪多个对象（例如在道路上奔跑的人、行驶的车辆等）。
如果有人能够指导我或提供示例代码供我尝试，我将不胜感激。
期待您的积极回复。 Shan
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;
bg.bShadowDetection = false;
int incr=0;
int track=0;
capture.open("E:/demo1.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;
}