3 using namespace suanzi;
7 static const int MaxLost = 5;
9 Tracker::Tracker(const cv::Mat& image, const Detection& detection, int id)
12 KF.transitionMatrix = (Mat_<double>(4, 4) <<
18 KF.measurementMatrix = (Mat_<double>(2, 2) <<
22 KF.processNoiseCov = 1e-5 * Mat_<double>::eye(4, 4);
23 KF.measurementNoiseCov = 1e-1 * Mat_<double>::ones(2, 2);
24 KF.errorCovPost = 1. * Mat_<double>::ones(4, 4);
25 randn(KF.statePre, Scalar::all(0), Scalar::all(0.1));
26 KF.statePost = (Mat_<double>(4, 1) << detection.center_x, detection.center_y, 0, 0);
34 void Tracker::updateState(const Mat& image)
36 preStatus = this->status;
37 int lost_age = this->age - this->last_active;
38 int active_age = this->last_active;
40 if (lost_age >= MaxLost){
41 status = TrackerStatus::Delete;
42 } else if (lost_age >= 1 && active_age == 1){
43 status = TrackerStatus::Delete;
44 } else if (lost_age >= 1) {
45 status = TrackerStatus::Lost;
49 void Tracker::addPatch(PatchPtr p)
51 patches.insert(patches.begin(), p);
52 if (patches.size() > MaxPatch){
53 patches.erase(patches.end());
57 void Tracker::correct(const cv::Mat& image, const Detection& detection)
59 // detection.center_x, detection.center_y,
60 // KF.correct(detect.center_x, detect.center_y);
62 status = TrackerStatus::Active;
66 void Tracker::predict()
70 Mat temp = KF.predict();