Information & Computer Science |
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Adaptive foreground and shadow segmentation using hidden conditional random fields |
CHU Yi-ping, YE Xiu-zi, QIAN Jiang, ZHANG Yin, ZHANG San-yuan |
School of Computer Science, State Key Lab. of CAD & CG, Zhejiang University, Hangzhou 310027, China |
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Abstract Video object segmentation is important for video surveillance, object tracking, video object recognition and video editing. An adaptive video segmentation algorithm based on hidden conditional random fields (HCRFs) is proposed, which models spatio-temporal constraints of video sequence. In order to improve the segmentation quality, the weights of spatio-temporal constraints are adaptively updated by on-line learning for HCRFs. Shadows are the factors affecting segmentation quality. To separate foreground objects from the shadows they cast, linear transform for Gaussian distribution of the background is adopted to model the shadow. The experimental results demonstrated that the error ratio of our algorithm is reduced by 23% and 19% respectively, compared with the Gaussian mixture model (GMM) and spatio-temporal Markov random fields (MRFs).
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Received: 15 August 2006
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