Open AccessHuman pose estimation method based on single depth image
Author(s) -
Wu Qingqiang,
Xu Guanghua,
Li Min,
Chen Longting,
Zhang Xin,
Xie Jun
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2017.0536
Subject(s) - pose , artificial intelligence , computer vision , computer science , 3d pose estimation , ground truth , point cloud , human skeleton , image (mathematics) , articulated body pose estimation , matching (statistics) , random forest , occlusion , pattern recognition (psychology) , mathematics , medicine , statistics , cardiology
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.