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

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

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.

Key words: computer vision, machine learning, pixel classification, depth image, pose estimation, point clouds

CLC Number:

TP391

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.

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