///////////////////////////////////////////////////////////////////////////////
|
// KalmanTracker.cpp: KalmanTracker Class Implementation Declaration
|
|
#include "KalmanTracker.h"
|
|
|
int KalmanTracker::kf_count = 0;
|
|
|
// initialize Kalman filter
|
void KalmanTracker::init_kf(StateType stateMat)
|
{
|
int stateNum = 7;
|
int measureNum = 4;
|
kf = KalmanFilter(stateNum, measureNum, 0);
|
|
measurement = cv::Mat::zeros(measureNum, 1, CV_32F);
|
|
kf.transitionMatrix = (cv::Mat_<float>(stateNum, stateNum) << 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1);
|
|
setIdentity(kf.measurementMatrix);
|
setIdentity(kf.processNoiseCov, Scalar::all(1e-2));
|
setIdentity(kf.measurementNoiseCov, Scalar::all(1e-1));
|
setIdentity(kf.errorCovPost, Scalar::all(1));
|
|
// initialize state vector with bounding box in [cx,cy,s,r] style
|
kf.statePost.at<float>(0, 0) = stateMat.x + stateMat.width / 2;
|
kf.statePost.at<float>(1, 0) = stateMat.y + stateMat.height / 2;
|
kf.statePost.at<float>(2, 0) = stateMat.area();
|
kf.statePost.at<float>(3, 0) = stateMat.width / stateMat.height;
|
}
|
|
|
// Predict the estimated bounding box.
|
StateType KalmanTracker::predict()
|
{
|
// predict
|
Mat p = kf.predict();
|
m_age += 1;
|
|
if (m_time_since_update > 0)
|
m_hit_streak = 0;
|
m_time_since_update += 1;
|
|
StateType predictBox = get_rect_xysr(p.at<float>(0, 0), p.at<float>(1, 0), p.at<float>(2, 0), p.at<float>(3, 0));
|
|
m_history.push_back(predictBox);
|
return m_history.back();
|
}
|
|
|
// Update the state vector with observed bounding box.
|
void KalmanTracker::update(StateType stateMat)
|
{
|
m_time_since_update = 0;
|
m_history.clear();
|
m_hits += 1;
|
m_hit_streak += 1;
|
|
// measurement
|
measurement.at<float>(0, 0) = stateMat.x + stateMat.width / 2;
|
measurement.at<float>(1, 0) = stateMat.y + stateMat.height / 2;
|
measurement.at<float>(2, 0) = stateMat.area();
|
measurement.at<float>(3, 0) = stateMat.width / stateMat.height;
|
|
// update
|
kf.correct(measurement);
|
}
|
|
|
// Return the current state vector
|
StateType KalmanTracker::get_state()
|
{
|
Mat s = kf.statePost;
|
return get_rect_xysr(s.at<float>(0, 0), s.at<float>(1, 0), s.at<float>(2, 0), s.at<float>(3, 0));
|
}
|
|
|
// Convert bounding box from [cx,cy,s,r] to [x,y,w,h] style.
|
StateType KalmanTracker::get_rect_xysr(float cx, float cy, float s, float r)
|
{
|
float w = sqrt(s * r);
|
float h = s / w;
|
float x = (cx - w / 2);
|
float y = (cy - h / 2);
|
|
if (x < 0 && cx > 0)
|
x = 0;
|
if (y < 0 && cy > 0)
|
y = 0;
|
|
return StateType(x, y, w, h);
|
}
|
|
|
|
/*
|
// --------------------------------------------------------------------
|
// Kalman Filter Demonstrating, a 2-d ball demo
|
// --------------------------------------------------------------------
|
|
const int winHeight = 600;
|
const int winWidth = 800;
|
|
Point mousePosition = Point(winWidth >> 1, winHeight >> 1);
|
|
// mouse event callback
|
void mouseEvent(int event, int x, int y, int flags, void *param)
|
{
|
if (event == CV_EVENT_MOUSEMOVE) {
|
mousePosition = Point(x, y);
|
}
|
}
|
|
void TestKF();
|
|
void main()
|
{
|
TestKF();
|
}
|
|
|
void TestKF()
|
{
|
int stateNum = 4;
|
int measureNum = 2;
|
KalmanFilter kf = KalmanFilter(stateNum, measureNum, 0);
|
|
// initialization
|
Mat processNoise(stateNum, 1, CV_32F);
|
Mat measurement = Mat::zeros(measureNum, 1, CV_32F);
|
|
kf.transitionMatrix = *(Mat_<float>(stateNum, stateNum) <<
|
1, 0, 1, 0,
|
0, 1, 0, 1,
|
0, 0, 1, 0,
|
0, 0, 0, 1);
|
|
setIdentity(kf.measurementMatrix);
|
setIdentity(kf.processNoiseCov, Scalar::all(1e-2));
|
setIdentity(kf.measurementNoiseCov, Scalar::all(1e-1));
|
setIdentity(kf.errorCovPost, Scalar::all(1));
|
|
randn(kf.statePost, Scalar::all(0), Scalar::all(winHeight));
|
|
namedWindow("Kalman");
|
setMouseCallback("Kalman", mouseEvent);
|
Mat img(winHeight, winWidth, CV_8UC3);
|
|
while (1)
|
{
|
// predict
|
Mat prediction = kf.predict();
|
Point predictPt = Point(prediction.at<float>(0, 0), prediction.at<float>(1, 0));
|
|
// generate measurement
|
Point statePt = mousePosition;
|
measurement.at<float>(0, 0) = statePt.x;
|
measurement.at<float>(1, 0) = statePt.y;
|
|
// update
|
kf.correct(measurement);
|
|
// visualization
|
img.setTo(Scalar(255, 255, 255));
|
circle(img, predictPt, 8, CV_RGB(0, 255, 0), -1); // predicted point as green
|
circle(img, statePt, 8, CV_RGB(255, 0, 0), -1); // current position as red
|
|
imshow("Kalman", img);
|
char code = (char)waitKey(100);
|
if (code == 27 || code == 'q' || code == 'Q')
|
break;
|
}
|
destroyWindow("Kalman");
|
}
|
*/
|
