Adaptive foreground and shadow segmentation using hidden conditional random fields

作     者：CHU Yi-ping YE Xiu-zi QIAN Jiang ZHANG Yin ZHANG San-yuan 

作者机构：School of Computer Science State Key Lab. of CAD ＆ CG Zhejiang University Hangzhou 310027 China 

出 版 物：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 (浙江大学学报（英文版）A辑（应用物理与工程）)

年 卷 期：2007年第8卷第4期

页      面：586-592页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：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) 

主　　题：Video segmentation Shadow elimination Hidden conditional random fields (HCRFs) On-line learning 

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).