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| Video Foreground and Shadow Automatic Segmentation Based on Discriminative Model |
| CHU Yi-Ping1,2, CHEN Qin1, HUANG Ye-Jue3, ZHANG San-Yuan2 |
1.College of Computer Science, Hangzhou Dianzi University, Hangzhou 3100182.
College of Computer Science and Technology, Zhejiang University, Hangzhou 3100273.
Department of Computer Science, Zhejiang Industry Polytechnic College, Shaoxing 312000 |
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Abstract Moving cast shadows are factors affecting segmentation quality. Efficient shadow detection and removal is a difficult problem in video segmentation. A method based on discriminative model for video foreground and shadow segmentation is proposed. It has capability of shadow detection and removal under different scenes. The proposed algorithm models background, shadows and foreground at pixel levels. These models are constrained by using 2-dimensional conditional random fields. Inference algorithm of probabilistic graphical models is adopted to obtain globally optimal segmentation results. The experimental results demonstrate the validity of the proposed algorithm, and the results are compared with other algorithms by using outdoor and indoor video data.
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Received: 04 June 2007
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2008, Vol. 21 
