static const int MaxLost = 5;
-Tracker::Tracker(const cv::Mat& image, const Detection& detection, int id) : id(id)
+Tracker::Tracker(const cv::Mat& image, const Detection& detection, int id)
+: id(id)
{
status = TrackerStatus::Fire;
preStatus = TrackerStatus::Fire;
// TODO: Kalman filter
- this->kf.transitionMatrix = (Mat_<double>(4, 4) <<
+ KF.transitionMatrix = (Mat_<double>(4, 4) <<
1, 0, 1, 0,
0, 1, 0, 1,
0, 0, 1, 0,
0, 0, 0, 1);
- this->kf.measurementMatrix = (Mat_<double>(2, 2) <<
+ KF.measurementMatrix = (Mat_<double>(2, 2) <<
1, 0, 0, 0,
0, 1, 0, 0);
- this->kf.processNoiseCov = 1e-5 * Mat_<double>::eye(4, 4);
- this->kf.measurementNoiseCov = 1e-1 * Mat_<double>::ones(2, 2);
- this->kf.errorCovPost = 1. * Mat_<double>::ones(4, 4);
+ KF.processNoiseCov = 1e-5 * Mat_<double>::eye(4, 4);
+ KF.measurementNoiseCov = 1e-1 * Mat_<double>::ones(2, 2);
+ KF.errorCovPost = 1. * Mat_<double>::ones(4, 4);
//this->kf.statePre = 0.1 * Matx_<int, 4, 1>::randn(4, 1);
//??? TODO
- randn(this->kf.statePre, Scalar::all(0), Scalar::all(0.1));
- this->kf.statePost = (Mat_<double>(4, 1) << detection.center_x, detection.center_y, 0, 0);
+ randn(KF.statePre, Scalar::all(0), Scalar::all(0.1));
+ KF.statePost = (Mat_<double>(4, 1) << detection.center_x, detection.center_y, 0, 0);
}
Tracker::~Tracker()
void Tracker::addPatch(PatchPtr p)
{
- this->patches.push_back(p);
+ patches.insert(patches.begin(), p);
+ if (patches.size() > MaxPatch){
+ patches.erase(patches.end());
+ }
}
void Tracker::correct(const cv::Mat& image, const Detection& detection)
{
- //kf.correct();
+ // detection.center_x, detection.center_y,
+ // KF.correct(detect.center_x, detect.center_y);
preStatus = status;
status = TrackerStatus::Active;
last_active = age;
}
+void Tracker::predict()
+{
+ age++;
+ //detection = KF.predict();
+}