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ORGM: Occlusion Relational Graphical Model for Human Pose Estimation
Fu, Lianrui1; Zhang, Junge1; Huang, Kaiqi1,2
2017-02-01
发表期刊IEEE TRANSACTIONS ON IMAGE PROCESSING
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
DOI10.1109/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
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/15238
专题智能感知与计算研究中心
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
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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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