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Actually I'm using "gaussian mixture based" "BackgroundSubtractorMOG2" to tracking cars in a video, but the result is a video in slowmotion and noise like car shadow. if someone know another algorithm with better performance, please answers me.

Actually I'm using "gaussian mixture based" "BackgroundSubtractorMOG2" to tracking cars in a video, but the result is a video in slowmotion and noise like car shadow. if someone know another algorithm with better performance, please answers me.me. this is the code:

cv::Mat frame;
cv::Mat back;
cv::Mat fore;
cv::VideoCapture cap("CarSurveillance/Video1.avi");
cv::BackgroundSubtractorMOG2 bg;
bg.setInt("nmixtures", 3);
bg.setBool("detectShadows", false);

std::vector<std::vector<cv::Point> > contours;

cv::namedWindow("Frame");
cv::namedWindow("Background");

while (1)
{
    cap.read(frame);
    bg.operator ()(frame, fore);
    bg.getBackgroundImage(back);
    cv::erode(fore, fore, cv::Mat());
    cv::dilate(fore, fore, cv::Mat());
    cv::findContours(fore, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
    cv::drawContours(frame, contours, -1, cv::Scalar(0, 0, 255), 2);
    cv::imshow("Frame", frame);
    cv::imshow("Background", back);
    if (cv::waitKey(30) >= 0) break;
}
return 0;

Actually I'm using "gaussian mixture based" "BackgroundSubtractorMOG2" to tracking cars in a video, but the result is a video in slowmotion and noise like car shadow. if someone know another algorithm with better performance, please answers me. this is the code: code:

cv::Mat frame;
cv::Mat back;
cv::Mat fore;
cv::VideoCapture cap("CarSurveillance/Video1.avi");
cv::BackgroundSubtractorMOG2 bg;
bg.setInt("nmixtures", 3);
bg.setBool("detectShadows", false);

std::vector<std::vector<cv::Point> > contours;

cv::namedWindow("Frame");
cv::namedWindow("Background");

while (1)
{
    cap.read(frame);
    bg.operator ()(frame, fore);
    bg.getBackgroundImage(back);
    cv::erode(fore, fore, cv::Mat());
    cv::dilate(fore, fore, cv::Mat());
    cv::findContours(fore, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
    cv::drawContours(frame, contours, -1, cv::Scalar(0, 0, 255), 2);
    cv::imshow("Frame", frame);
    cv::imshow("Background", back);
    if (cv::waitKey(30) >= 0) break;
}
return 0;

Actually I'm using "gaussian mixture based" "BackgroundSubtractorMOG2" to tracking cars in a video, but the result is a video in slowmotion and noise like car shadow. if someone know another algorithm with better performance, please answers me. this is the code:

cv::Mat frame;
cv::Mat back;
cv::Mat fore;
cv::VideoCapture cap("CarSurveillance/Video1.avi");
cv::BackgroundSubtractorMOG2 bg;
bg.setInt("nmixtures", 3);
bg.setBool("detectShadows", false);

std::vector<std::vector<cv::Point> > contours;

cv::namedWindow("Frame");
cv::namedWindow("Background");

while (1)
{
    cap.read(frame);
    bg.operator ()(frame, fore);
    bg.getBackgroundImage(back);
    cv::erode(fore, fore, cv::Mat());
    cv::dilate(fore, fore, cv::Mat());
    cv::findContours(fore, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
    cv::drawContours(frame, contours, -1, cv::Scalar(0, 0, 255), 2);
    cv::imshow("Frame", frame);
    cv::imshow("Background", back);
    if (cv::waitKey(30) >= 0) break;
}
return 0;

//bg.getBackgroundImage(back); 
cv::Mat element = getStructuringElement(MORPH_RECT, Size(3, 3), Point(1, 1));
cv::erode(fore, fore, element); 
element = getStructuringElement(MORPH_RECT, Size(9, 9), Point(4, 4)); 
cv::dilate(fore, fore, element);