卡尔曼滤波器的多目标跟踪 [英] Multiple Object Tracking with Kalman Filter
问题描述
我是OpenCV的新手.我一直希望学习新的图像处理技术/编程.我看过一些关于对象检测，跟踪，计数等的教程. 我希望能学到同样的东西，并尝试做自己的类似项目.
I am new on OpenCV. I am always wish to learn new image processing technologies / Programming. I have seen few tutorial on Object detection, tracking, counting etc. I wish to learn the same and try to make my own similar project.
在互联网和报纸上进行了大量搜索.最后，我了解了用于对象跟踪的卡尔曼滤波器.我已经按照以下方式使用了以下代码:
With lot of searching on internet and papers. Finally i came to know about Kalman Filter for object tracking. I have used following codes as per following:
- 背景扣除
- 平滑，模糊等滤镜.
- 找到轮廓
- 绘制矩形并找到质心.
- 应用卡尔曼过滤器
现在，我可以使用我的代码跟踪一个对象.我想跟踪多个对象.(例如，在路上行驶的人，在行驶的车辆等)
Now, i can track ONE Object with my codes. I want to track multiple objects.(i.e. people running on the roads, vehicle running etc.)
如果有人可以指导我或给我示例代码进行尝试，我将感到高兴和感谢.
I would be pleased and appreciate if someone can guide me or give me example codes to try with.
期待您的肯定答复. 珊
Looking forward your positive reply. 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;
}
推荐答案
如果要跟踪多个不相关对象，则只需为要跟踪的每个目标复制恒速卡尔曼滤波器.这将是最有效的方法.
If you are tracking multiple unrelated objects you can just replicate your constant-velocity Kalman filter for every target you are tracking. This would be the most efficient way to do it.
如果您认为目标之间存在某种关联(例如，测量目标时存在错误，则有一个共同的组成部分，或者一个目标的行为会影响您对另一个目标的预测)，则可以将它们组合在一起.您将采用现有的4x4矩阵(x，y，vx，vy)，并以块对角线的方式将它们组合起来以形成更大的矩阵.较大的协方差矩阵现在将具有指示一种测量的不确定性与另一种测量如何相关的区域.假设您认为您的相机可能在晃动.然后，您会期望所有对象可能具有相同的角噪声.您可以将该概念包含在R矩阵中，并使用非对角线术语将所有测量值捆绑在一起.这种形式的计算成本会更高(由于矩阵数学的非线性成本)，并且在添加或删除目标时也会增加复杂性.
If you thought your targets were related in some way (e.g. that error in measuring them had a common component, or if the behavior of one would influence your prediction of the behavior of another) then you could combine them all together. You would take your existing 4x4 matrix (x, y, vx, vy) and combine them in a block-diagonal fashion to make a larger matrix. The larger covariance matrix would now have regions that indicate how uncertainty of one measurement is related to another. Let's say you think your camera may be shaking. You would then expect all objects might have the same angular noise. You could include that idea in your R matrix with off-diagonal terms tying all the measurements together. This form will be much more costly computationally (because of the nonlinear cost of matrix math) and would also add complexity when adding or removing targets.
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