ORGM: Occlusion Relational Graphical Model for Human Pose Estimation

doi:10.1109/TIP.2016.2639441

CASIA OpenIR > 模式识别实验室

	ORGM: Occlusion Relational Graphical Model for Human Pose Estimation
	Fu, Lianrui1 ; Zhang, Junge1 ; Huang, Kaiqi1,2
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2017-02-01
卷号	26 期号:2 页码:927-941
文章类型	Article
摘要	Articulated human pose estimation from monocular image is a challenging problem in computer vision. Occlusion is a main challenge for human pose estimation, which is largely ignored in popular tree structured models. The tree structured model is simple and convenient for exact inference, but short in modeling the occlusion coherence especially in the case of self-occlusion. We propose an occlusion relational graphical model, which is able to model both self-occlusion and occlusion by the other objects simultaneously. The proposed model can encode the interactions between human body parts and objects, and enables it to learn occlusion coherence from data discriminatively. We evaluate our model on several public benchmarks for human pose estimation, including challenging subsets featuring significant occlusion. The experimental results show that our method is superior to the previous state-of-the-arts, and is robust to occlusion for 2D human pose estimation.
关键词	Occlusion Pose Estimation Spacial Relationship Mixture Graphical Model
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TIP.2016.2639441
关键词[WOS]	PICTORIAL STRUCTURES ; FLEXIBLE MIXTURES ; PARTS
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61322209 ; International Partnership Program of the Chinese Academy of Science(173211KYSB20160008) ; 61673375 ; 61403387)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000404773100025
引用统计	被引频次：20[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/15238
专题	模式识别实验室
作者单位	1.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100049, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Fu, Lianrui,Zhang, Junge,Huang, Kaiqi. ORGM: Occlusion Relational Graphical Model for Human Pose Estimation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2017,26(2):927-941.
APA	Fu, Lianrui,Zhang, Junge,&Huang, Kaiqi.(2017).ORGM: Occlusion Relational Graphical Model for Human Pose Estimation.IEEE TRANSACTIONS ON IMAGE PROCESSING,26(2),927-941.
MLA	Fu, Lianrui,et al."ORGM: Occlusion Relational Graphical Model for Human Pose Estimation".IEEE TRANSACTIONS ON IMAGE PROCESSING 26.2(2017):927-941.

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