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Beyond Tree Structure Models: A New Occlusion Aware Graphical Model for Human Pose Estimation
Fu LR(付连锐); Junge Zhang; Kaiqi Huang
2015-12
Conference NameIEEE International Conference on Computer Vision
Source PublicationProceeding of IEEE International Conference on Computer Vision
Pages1976-1984
Conference Date2015.12.11-2015.12.18
Conference PlaceSantiago, Chile
AbstractOcclusion is a main challenge for human pose estimation, which is largely ignored in popular tree structure models. The tree structure 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 aware graphical model which is able to model both self-occlusion and occlusion by the other objects simultaneously. The proposed model structure can encodes the interactions between human body parts and objects, and hence 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 obtains comparable accuracy with the state-of-the-arts, and is robust to occlusion for 2D human pose estimation
KeywordGraphical Model
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11648
Collection智能感知与计算研究中心
Corresponding AuthorKaiqi Huang
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Fu LR,Junge Zhang,Kaiqi Huang. Beyond Tree Structure Models: A New Occlusion Aware Graphical Model for Human Pose Estimation[C],2015:1976-1984.
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