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Multiple Video Tracking

I am working on a small image processing destination where I need to track 4 red objects. I have how to track one. I want to know what is best for tracking more than one point.

There are 4 points that are positioned to form a rectangle, so I can use a shape definition or angle definition to detect and track points. See image below.

enter image description here

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3 answers

My naive implementation uses the method described in the OpenCV bounding box for tracking red spots.

:

/* get_positions: a function to retrieve the center of the detected blobs.
 * largely based on OpenCV "Creating Bounding boxes and circles for contours" tutorial.
 */
std::vector<cv::Point2f> get_positions(cv::Mat& image)
{
    if (image.channels() > 1)
    {
        std::cout << "get_positions: !!! Input image must have a single channel" << std::endl;
        return std::vector<cv::Point2f>();
    }

    std::vector<std::vector<cv::Point> > contours;
    cv::findContours(image, contours, cv::RETR_LIST, cv::CHAIN_APPROX_SIMPLE);

    // Approximate contours to polygons and then get the center of the objects
    std::vector<std::vector<cv::Point> > contours_poly(contours.size());
    std::vector<cv::Point2f> center(contours.size());
    std::vector<float> radius(contours.size());
    for (unsigned int i = 0; i < contours.size(); i++ )
    {
        cv::approxPolyDP(cv::Mat(contours[i]), contours_poly[i], 5, true );
        cv::minEnclosingCircle((cv::Mat)contours_poly[i], center[i], radius[i]);
    }

    return center;
}

, -. , @Dennis (, , ).

, .

int main()
{
    // Open the capture device. My webcam ID is 0:
    cv::VideoCapture cap(0);
    if (!cap.isOpened())
    {
        std::cout << "!!! Failed to open webcam" << std::endl;
        return -1;
    }

    // Let create a few window titles for debugging purposes
    std::string wnd1 = "Input", wnd2 = "Red Objs", wnd3 = "Output";   

    // These are the HSV values used later to isolate RED-ish colors
    int low_h = 160, low_s = 140, low_v = 50;
    int high_h = 179, high_s = 255, high_v = 255;

    cv::Mat frame, hsv_frame, red_objs;
    while (true)
    {
        // Retrieve a new frame from the camera
        if (!cap.read(frame))
            break;

        cv::Mat orig_frame = frame.clone();
        cv::imshow(wnd1, orig_frame);

orig_frame:

enter image description here

        // Convert BGR frame to HSV to be easier to separate the colors
        cv::cvtColor(frame, hsv_frame, CV_BGR2HSV);

        // Isolate red colored objects and save them in a binary image
        cv::inRange(hsv_frame,
                    cv::Scalar(low_h,  low_s,  low_v),
                    cv::Scalar(high_h, high_s, high_v),
                    red_objs);

        // Remove really small objects (mostly noises)
        cv::erode(red_objs, red_objs, cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3)));
        cv::dilate(red_objs, red_objs, cv::getStructuringElement(cv::MORPH_RECT, cv::Size(7, 7)));

        cv::Mat objs = red_objs.clone();
        cv::imshow(wnd2, objs);

objs:

enter image description here

        // Retrieve a vector of points with the (x,y) location of the objects
        std::vector<cv::Point2f> points = get_positions(objs);

        // Draw a small green circle at those locations for educational purposes
        for (unsigned int i = 0; i < points.size(); i++)
            cv::circle(frame, points[i], 3, cv::Scalar(0, 255, 0), -1, 8, 0);

        cv::imshow(wnd3, frame);

enter image description here

        char key = cv::waitKey(33);
        if (key == 27) {   /* ESC was pressed */
            //cv::imwrite("out1.png", orig_frame);
            //cv::imwrite("out2.png", red_objs);
            //cv::imwrite("out3.png", frame);
            break;
        }
    }

    cap.release();

    return 0;
}
+4

:
:

This is the basis of the algorithm. Then you have to handle situations where drops introduce an image (this is the case when there are drops in the current frame but there is no close blob from the previous frame) or leave the image (there is a drop in the previous frame, but there is no close blob in the current frame).

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