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Received: 2006-08-15

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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.4 P.586-592


Adaptive foreground and shadow segmentation using hidden conditional random fields

Author(s):  CHU Yi-ping, YE Xiu-zi, QIAN Jiang, ZHANG Yin, ZHANG San-yuan

Affiliation(s):  School of Computer Science, State Key Lab. of CAD & CG, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   hzcyp@yahoo.com.cn, yxz@cs.zju.edu.cn

Key Words:  Video segmentation, Shadow elimination, Hidden conditional random fields (HCRFs), On-line learning

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 A, 2007, 8(4): 586-592.

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author="CHU Yi-ping, YE Xiu-zi, QIAN Jiang, ZHANG Yin, ZHANG San-yuan",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

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%T Adaptive foreground and shadow segmentation using hidden conditional random fields
%A CHU Yi-ping
%A YE Xiu-zi
%A QIAN Jiang
%A ZHANG San-yuan
%J Journal of Zhejiang University SCIENCE A
%V 8
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%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A0586

T1 - Adaptive foreground and shadow segmentation using hidden conditional random fields
A1 - CHU Yi-ping
A1 - YE Xiu-zi
A1 - QIAN Jiang
A1 - ZHANG Yin
A1 - ZHANG San-yuan
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 4
SP - 586
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%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.A0586

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).

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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