Region-based Moving Shadow Detection using Affinity Propagation

Moving shadow detection is a challenging task in computer vision applications, such as surveillance, video conference, visual tracking, object recognition, and many other important applications. In this paper, region-based moving shadow detection using affinity propagation (RMSDAP) is presented, which detects shadows in terms of texture similarity. Firstly, we divide foreground image into no overlapping blocks and extract color features from each block. Secondly, affinity propagation is utilized to cluster foreground blocks adaptively and sub regions are generated after coarse segmentation. Specially, each sub region has the characteristics of regional uniformity. Finally, we extract texture feature from irregular sub regions while calculate texture similarity and normalized correlation coefficient simultaneously for each sub region to classify moving shadows. Extensive experiments demonstrate that RMSDAP is superior to some well-known methods especially pixel-based methods. In particular, our method exhibits much better performance compared with fixed block method, which can maintain the texture consistency in one region adequately.