Adaptive foreground edge extraction from video stream

We propose a new method to extract foreground edges in a video streams taken from a stationary camera. Our background model is based on the fact that a background pixel's gradient components follow Gaussian mixture model(GMM). GMM is performed on the initial group of video frames to obtain the initial pixel gradient component distribution information at each pixel. Then each of the current Canny edge pixels is classified into foreground or background pixel based on its gradient components' weighted square sum of distances from their respective mean values. If the difference is larger than a threshold, it is then classified as a foreground pixel, otherwise a background pixel in which case the GMM information is accordingly updated. If the ratio of the number of foreground pixels over the total number of Canny edge pixel is large than a certain threshold, a new GMM background modeling is trigger. The algorithm is implemented in Visual C++ and tested on a laptop powered by an Intel Pentium 3.0GHz. The experiment shows the algorithm is highly selective in extracting valid foreground edge pixels and it's speed is 43 ms/frame for a video stream of 640×480 and shows that the method is applicable for real-time processing.