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Human Pose Estimation Using Exemplars and Part Based Refinement
 

Summary: Human Pose Estimation
Using Exemplars and Part Based Refinement
Yanchao Su1
, Haizhou Ai1
, Takayoshi Yamashita2
, and Shihong Lao2
1
Computer Science and Technology Department, Tsinghua, Beijing 100084, China
2
Core Technology Center, Omron Corporation, Kyoto 619-0283, Japan
Abstract. In this paper, we proposed a fast and accurate human pose
estimation framework that combines top-down and bottom-up methods.
The framework consists of an initialization stage and an iterative search-
ing stage. In the initialization stage, example based method is used to
find several initial poses which are used as searching seeds of the next
stage. In the iterative searching stage, a larger number of body parts can-
didates are generated by adding random disturbance to searching seeds.
Belief Propagation (BP) algorithm is applied to these candidates to find
the best n poses using the information of global graph model and part
image likelihood. Then these poses are further used as searching seeds

  

Source: Ai, Haizhou - Department of Computer Science and Technology, Tsinghua University

 

Collections: Computer Technologies and Information Sciences