Monocular Depth Image Mark-less Pose Estimation Based on Feature Regression

Authors

Chen Ying, Key Laboratory of Advanced Control Light Process, Jiangnan University, Wuxi 214000, China;
Shen Li, Key Laboratory of Advanced Control Light Process, Jiangnan University, Wuxi 214000, China;

Abstract

Abstract: Monocular camera mark-less pose estimation system suffers low accuracy, robustness and efficiency due to variety of action, self-occlusion of human body. A method of feature exaction from point clouds was proposed, in which a single-to-multiple (S2M) feature regressor and a joint position regressor were designed to quickly and accurately predict the 3D positions of body joints from a single depth image without any temporal information. Experiment result shows that the estimation accuracy is superior to that of state-of-the-arts and multi-camera based methods.

Recommended Citation

Ying, Chen and Li, Shen (2020) "Monocular Depth Image Mark-less Pose Estimation Based on Feature Regression," Journal of System Simulation: Vol. 32: Iss. 2, Article 14.
DOI: 10.16182/j.issn1004731x.joss.18-0143
Available at: https://dc-china-simulation.researchcommons.org/journal/vol32/iss2/14

First Page

269

Revised Date

2018-07-03

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.18-0143

Last Page

277

CLC

TP391

Recommended Citation

Chen Ying, Shen Li. Monocular Depth Image Mark-less Pose Estimation Based on Feature Regression[J]. Journal of System Simulation, 2020, 32(2): 269-277.

DOI

10.16182/j.issn1004731x.joss.18-0143