Title
A Sequential Approach To 3d Human Pose Estimation: Separation Of Localization And Identification Of Body Joints
Abstract
In this paper, we propose a new approach to 3D human pose estimation from a single depth image. Conventionally, 3D human pose estimation is formulated as a detection problem of the desired list of body joints. Most of the previous methods attempted to simultaneously localize and identify body joints, with the expectation that the accomplishment of one task would facilitate the accomplishment of the other. However, we believe that identification hampers localization; therefore, the two tasks should be solved separately for enhanced pose estimation performance. We propose a two-stage framework that initially estimates all the locations of joints and subsequently identifies the estimated joints for a specific pose. The locations of joints are estimated by regressing K closest joints from every pixel with the use of a random tree. The identification of joints are realized by transferring labels from a retrieved nearest exemplar model. Once the 3D configuration of all the joints is derived, identification becomes much easier than when it is done simultaneously with localization, exploiting the reduced solution space. Our proposed method achieves significant performance gain on pose estimation accuracy, thereby improving both localization and identification. Experimental results show that the proposed method exhibits an accuracy significantly higher than those of previous approaches that simultaneously localize and identify the body parts.
Year
DOI
Venue
2016
10.1007/978-3-319-46454-1_45
COMPUTER VISION - ECCV 2016, PT V
Keywords
Field
DocType
Depth camera, Human pose, Regression forest
Random tree,Computer vision,Exemplar theory,Computer science,Pose,Pixel,Artificial intelligence,Body joints
Conference
Volume
ISSN
Citations 
9909
0302-9743
3
PageRank 
References 
Authors
0.42
16
4
Name
Order
Citations
PageRank
Ho Yub Jung1294.73
Yumin Suh2494.38
Gyeongsik Moon3172.97
Kyoung Mu Lee43228153.84