1.State Key Laboratory of CAD&CG, College of Computer Science and Technology, Zhejiang University, Hangzhou 3100272. College of Computer, Hangzhou Dianzi University, Hangzhou 3100183. Department of Computer, Zhejiang Industry Polytechnic College, Shaoxing 312000
Abstract Video segmentation is important for video object tracking, counting and recognition. Shadows are the factors that affect the accuracy of object segmentation. Efficiently detecting and removing the shadows can improve the quality of object segmentation. An algorithm for video-foreground and shadow segmentation is proposed in this paper. It models shadows with state machine and the shadows are removed according to the shadow models. The potential functions for the background, shadow and foreground are defined. The spatiotemporal neighboring relationships in the video sequence are constructed by using Markov random fields. Gibbs sampling algorithm is adopted to solve the MAP problem and thus the segmentation quality is improved . The correctness of the proposed algorithm is tested under different environments and the results demonstrate the validity of the algorithm compared with other algorithms.
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2008, Vol. 21 
