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ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2007, Vol. 8 Issue (4): 586-592    DOI: 10.1631/jzus.2007.A0586
Information & Computer Science     
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).

Key words： Video segmentation      Shadow elimination      Hidden conditional random fields (HCRFs)      On-line learning     
Received: 15 August 2006     
CLC:  TP391.7  
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YE Xiu-zi
CHU Yi-ping
QIAN Jiang
ZHANG Yin
ZHANG San-yuan
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CHU Yi-ping, YE Xiu-zi, QIAN Jiang, ZHANG Yin, ZHANG San-yuan. Adaptive foreground and shadow segmentation using hidden conditional random fields. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2007, 8(4): 586-592.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.2007.A0586     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2007/V8/I4/586

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