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ORGM: Occlusion Relational Graphical Model for Human Pose Estimation
Fu, Lianrui1; Zhang, Junge1; Huang, Kaiqi1,2
AbstractArticulated 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.
KeywordOcclusion Pose Estimation Spacial Relationship Mixture Graphical Model
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China(61322209 ; International Partnership Program of the Chinese Academy of Science(173211KYSB20160008) ; 61673375 ; 61403387)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000404773100025
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Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.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
Recommended Citation
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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