Target tracking of opencv3 (single target, multi-target)

For opencv players who are just getting started, when they mention target tracking, they immediately think of camshift, but camshift tracking often fails to meet our tracking requirements, including stability and accuracy.

After the release of opencv3.1, it integrates multiple tracking algorithms, namely trackers, most of which are well-known algorithms on the VOT competition list in recent years. Although there are still defects, the effect is not bad.

ps: I saw an introduction to target tracking on Zhihu, and it feels good. The link is here!

Single target tracking is very simple, put an official routine for reference (opencv official):

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

Among them,  the creation of the tracker can be selected from the following types, which represent the tracking algorithm used;

• ONE THOUSAND
• BOOSTING
• MEDIANFLOW
• TLD
• KCF

eg-- Ptr<Tracker> tracker = Tracker::create("KCF"); 

Multi-target tracking uses MultiTracker, such as MultiTracker myTracker("KCF") , pay attention to two points, add target with its member function myTracker.add(Mat src, Rect2d roi), get tracking result use myTracker.update( Mat src, vector <Rect2d> targets ), the serial number of the tracking result is the serial number of the vector.

The following code is to track the target selected by the mouse, and the number of targets to be tracked is about 10 (in principle, there is no upper limit, but my computer is relatively bad, and if there are too many targets, it will be stuck)#include <stdafx h=""> #include "opencv2/opencv.hpp" #include <vector> using namespace cv; using namespace std; bool selectObject = false; Point origin;//用于保存鼠标选择第一次单击时点的位置 Rect selection;//用于保存鼠标选择的矩形框 int trackObject = 0; /*------------------------------- 函数功能 显示轨迹 --------------------------------*/ void displayTrajectory(Mat img, vector<point> traj, Scalar s) { if (!traj.empty()) { for (size_t i = 0; i < traj.size() - 1; i++) { line(img, traj[i], traj[i + 1], s, 2, 8, 0); } } } /*------------------------------- 鼠标控制 --------------------------------*/ void onMouse(int event, int x, int y, int, void*) { if (selectObject)//只有当鼠标左键按下去时才有效，然后通过if里面代码就可以确定所选择的矩形区域selection了 { selection.x = MIN(x, origin.x);//矩形左上角顶点坐标 selection.y = MIN(y, origin.y); selection.width = std::abs(x - origin.x);//矩形宽 selection.height = std::abs(y - origin.y);//矩形高 selection &= Rect(0, 0, src.cols, src.rows);//用于确保所选的矩形区域在图片范围内 //rectangle(src,selection,Scalar(0,0,255),2); } switch (event) { case CV_EVENT_LBUTTONDOWN: origin = Point(x, y); selection = Rect(x, y, 0, 0);//鼠标刚按下去时初始化了一个矩形区域 selectObject = true; break; case CV_EVENT_LBUTTONUP: selectObject = false; if (selection.width > 0 && selection.height > 0) trackObject = -1; break; } } int main(int argc, char *argv[]) { namedWindow("tracker"); setMouseCallback("tracker", onMouse(), 0); MultiTracker myTracker("KCF"); vector<vector<point>> trajectorys;//轨迹记录 int objectCounter = 0; VideoCapture cap(0);//打开默认的摄像头 if (!cap.isOpened()) { return -1; } Mat src; bool stop = false; while (!stop) { cap>>src; if (selectObject) { rectangle(src, selection, Scalar(0, 0, 255), 2, 8, 0); } if (trackObject < 0) { myTracker.add(src, selection); Point cen = Point(selection.x + selection.width / 2.0, selection.y + selection.height / 2.0); trajectorys.resize(objectCounter + 1); trajectorys[objectCounter].push_back(cen);//从[0]开始初始化轨迹起始点 objectCounter++;//待跟踪目标个数加1 trackObject = 1; } if (trackObject) { vector<rect2d> r; myTracker.update(src, r); size_t s = r.size(); RNG rng; for (size_t i = 0; i < s; i++) { Scalar scalar = Scalar(rng.uniform(0, 256), rng.uniform(0, 256), rng.uniform(0, 256)); rectangle(src, r[i], scalar, 2, 8, 0); Point C = Point(r[i].x + r[i].width / 2.0, r[i].y + r[i].height / 2.0); trajectorys[i].push_back(C); displayTrajectory(src, trajectorys[i], scalar); char name[10]; sprintf_s(name, "%d", (i + 1)); putText(src, name, r[i].tl(), 3, 0.8, Scalar(0, 255, 255), 2, 8, false); } } imshow("tracker", src); if (waitKey(30) == 27) //Esc键退出 { stop = true; } } return 0; } </rect2d></point></point></vector></stdafx>