Background subtraction in OpenCV(C++)

Question

I want to implement a background averaging method. I have 50 frames of images taken in one second and some of the frames contain lightning which I want to extract as the foreground. The frames are taken with a stationary camera and the frames are taken as grayscales. What I want to do is:

1. Get the background model
2. After, compare each frame to the background model to determine whether there is lighting in that frame or not.

I read some documents on how this can possible be done by using cvAcc() but am having a difficulty understanding how this can be done. I would appreciate a piece of code which guide me and links to documents that can help me understand how I can implement this.

Thanking you in advance.

Answer 1

We had the same task in one of our projects.

To get the background model, we simply create a class BackgroundModel, capture the first (lets say) 50 frames and calculate the average frame to avoid pixel errors in the background model.

For example, if you get an 8-bit greyscale image (CV_8UC1) from your camera, you initialize your model with CV_16UC1 to avoid clipping.

cv::Mat model = cv::Mat(HEIGHT, WIDTH, CV_16UC1, cv::Scalar(0));

Now, waiting for the first frames to calculate your model, just add every frame to the model and count the amount of received frames.

void addFrame(cv::Mat frame) {
    cv::Mat convertedFrame;
    frame.convertTo(convertedFrame, CV_16UC1);
    cv::add(convertedFrame, model, model);
    if (++learnedFrames >= FRAMES_TO_LEAN) { // FRAMES_TO_LEARN = 50
        createMask();
    }
}

The createMask() function calculates the average frame which we use for the model.

void createMask() {
    cv::convertScaleAbs(model, mask, 1.0 / learnedFrames);
    mask.convertTo(mask, CV_8UC1);
}

Now, you just send all the frames the way through the BackgroundModel class to a function subtract(). If the result is an empty cv::Mat, the mask is still calculated. Otherwise, you get a subtracted frame.

cv::Mat subtract(cv::Mat frame) {
    cv::Mat result;
    if (++learnedFrames >= FRAMES_TO_LEAN) { // FRAMES_TO_LEARN = 50
        cv::subtract(frame, mask, result);
    }
    else {
        addFrame(frame);
    }
    return result;
}

Last but not least, you can use Scalar sum(const Mat& mtx) to calculate the pixel sum and decide if it's a frame with lights on it.