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	Recovery of upper body poses in static images based on joints detection
	Hu, Zhilan ; Wang, Guijin ; Lin, Xinggang ; Yan, Hong
	2010-10-12 ; 2010-10-12
关键词	Pose estimation Markov chain Monte Carlo Torso detection Gaussian mixture model MODEL Computer Science, Artificial Intelligence
中文摘要	Recovering human body poses from static images is challenging without prior knowledge of pose, appearance, background and clothing. In this paper, we propose a novel model-based upper poses recovery method via effective joints detection. In our research, three observables are firstly detected: face, skin, and torso. Then the joints are properly initialized according to the observables and some heuristic configuration constraints. Finally the sample-based Markov chain Monte Carlo (MCMC) method is employed to determine the final pose. The main contributions of this paper include a robust torso detector through maximizing a posterior estimation, effective joints initialization, and two continuous likelihood functions developed for effective pose inference. Experiments on 250 real world images show that our method can accurately recover upper body poses from images with a variety of individuals, poses, backgrounds and clothing. (c) 2008 Elsevier B.V. All rights reserved.
语种	英语 ; 英语
出版者	ELSEVIER SCIENCE BV ; AMSTERDAM ; PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/82702]
专题	清华大学
推荐引用方式 GB/T 7714	Hu, Zhilan,Wang, Guijin,Lin, Xinggang,et al. Recovery of upper body poses in static images based on joints detection[J],2010, 2010.
APA	Hu, Zhilan,Wang, Guijin,Lin, Xinggang,&Yan, Hong.(2010).Recovery of upper body poses in static images based on joints detection..
MLA	Hu, Zhilan,et al."Recovery of upper body poses in static images based on joints detection".(2010).

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