NDLI: Human pose estimation method based on single depth image

Content Provider	IET Digital Library
Author	Wu, Qingqiang Xu, Guanghua Li, Min Chen, Longting Zhang, Xin Xie, Jun
Abstract	Many of current human pose estimation methods based on depth images require training stage. However, the training stage costs huge work on making samples. And many methods for human pose occlusion condition cannot work well. In this study, a novel approach to estimate human pose with a depth image called model-based recursive matching (MRM) is introduced. A human skeleton model with customised parameters is created based on T-pose to fit different body types. The authors use depth image and 3D point cloud corresponding to input. In contrast to previous work, the proposed method avoids training step and can give an accurate estimation in the case of the human occlusion condition. They demonstrate the method by comparing to the method Kinect offered by using random forest on 20 human poses. And the ground truth of coordinates of pose joint is made by the motion capture system. The result shows that the proposed method not only works well on the general human pose but also can deal with human occlusion better. And the authors’ method can be also applied to the disabled people and other creatures.
Starting Page	919
Ending Page	924
Page Count	6
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 6, Sep (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2017.0536
Journal	IET Computer Vision
Publisher Date	2018-04-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	3D Point Cloud Computer Vision And Image Processing Technique Human Pose Estimation Method Human Pose Occlusion Condition Human Skeleton Model Image Matching Image Motion Analysis Image Recognition Kinect Method Model-based Recursive Matching Motion Capture System MRM Pose Estimation Pose Joint Single Depth Image T-pose Training Stage
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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