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Context Aware Model for Articulated Human Pose Estimation
Fu LR(付连锐); Junge Zhang; Kaiqi Huang
2015-09
会议名称IEEE Conference on Image Processing
会议录名称Proceeding of IEEE Conference on Image Processing
会议日期2015.9.27-2015.9.30
会议地点Quebec, Canada
摘要Simple 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
关键词Context Aware Model
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11649
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
推荐引用方式
GB/T 7714
Fu LR,Junge Zhang,Kaiqi Huang. Context Aware Model for Articulated Human Pose Estimation[C],2015.
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