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Context Aware Model for Articulated Human Pose Estimation
Fu LR(付连锐); Junge Zhang; Kaiqi Huang
Conference NameIEEE Conference on Image Processing
Source PublicationProceeding of IEEE Conference on Image Processing
Conference Date2015.9.27-2015.9.30
Conference PlaceQuebec, Canada
AbstractSimple tree model prevails for 2D pose estimation for its simplicity and efficiency. However, the limited kinetic constraints often lead to double-counting and damage the accuracy of leaf parts, and this is largely ignored in previous work. In this paper, we propose a novel enhanced tree model which incorporates both local kinetic constraints and global contextual constraints among non-adjacent parts. By introducing virtual parts, we are able to model richer constraints within a tree structure and dynamic programming can be utilized for efficient inference. Experiments on public benchmarks show that our method is more effective in tackling double counting problem and can improve the localization accuracy, especially for the challenging lower limbs
KeywordContext Aware Model
Document Type会议论文
Corresponding AuthorKaiqi Huang
Recommended Citation
GB/T 7714
Fu LR,Junge Zhang,Kaiqi Huang. Context Aware Model for Articulated Human Pose Estimation[C],2015.
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