Background Subtraction based on Mutual Information

Motion Detection is major task in every application of computer vision such as video surveillance. Background Subtraction (BS) algorithms have been employed for several years to find a moving objects in a scene. BS has been used in video surveillance due to its simplicity and the fact that cameras are stationary in a video surveillance systems. The present paper introduce a new approach to motion detection through a hierarchical model that uses Mutual Information as a measure of change in the scene. Since pixels in a frame belong to objects, a segmentation in regions of the frame is done by mean-shift algorithm. Then a Mutual information measurement between the segmented region and the incoming frame is performed. A first approach to foreground mask is achieved and later is refined using a modification of the Wronskian Change Detector (WCD). The experimental results show that our proposed algorithm improve the performance in comparison with a pixel based Background Subtraction algorithm mixture of gaussians (MoG), a hierarchical block based Background Subtraction algorithm (HMDRP) and a test of linear independence (WCD).