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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
会议名称IEEE International Conference on Computer Vision
会议录名称Proceeding of IEEE International Conference on Computer Vision
页码1976-1984
会议日期2015.12.11-2015.12.18
会议地点Santiago, Chile
摘要Occlusion 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
关键词Graphical Model
收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11648
专题智能感知与计算研究中心
通讯作者Kaiqi Huang
作者单位中国科学院自动化研究所
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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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