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 |
第一作者单位 | 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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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
[ICIP15_Fu]Context a(1297KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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