| Adaptive foreground and shadow segmentation using hidden conditional random fields |
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| 作者姓名： | CHU Yi-ping YE Xiu-zi QIAN Jiang ZHANG Yin ZHANG San-yuan |
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| 作者单位： | School of Computer Science, State Key Lab. of CAD ＆ CG, Zhejiang University, Hangzhou 310027, China |
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| 基金项目： | Project supported by the National Natural Science Foundation of China (Nos. 60473106, 60273060 and 60333010)，the Ministry of Education of China (No. 20030335064)，the Education Depart-ment of Zhejiang Province, China (No. G20030433) |
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| 摘 要： | 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 con- straints 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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| 关 键 词： | 自适应前景分割 视频分割 阴影消除 隐藏有条件随机场 |
| 收稿时间： | 2006-08-15 |
| 修稿时间： | 2006-10-25 |
Adaptive foreground and shadow segmentation using hidden conditional random fields |
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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[J].Journal of Zhejiang University Science,2007,8(4):586-592. |
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| Authors: | Chu Yi-ping Ye Xiu-zi Qian Jiang Zhang Yin Zhang San-yuan |
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| Institution: | (1) 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 con- straints 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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| Keywords: | Video segmentation Shadow elimination Hidden conditional random fields (HCRFs) On-line learning |
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