Human Pose Recognition Based on Depth Image Multifeature Fusion

Haikuan Wang, Feixiang Zhou, Wenju Zhou and Ling Chen

Complexity, 2018, vol. 2018, 1-12

Abstract:

The recognition of human pose based on machine vision usually results in a low recognition rate, low robustness, and low operating efficiency. That is mainly caused by the complexity of the background, as well as the diversity of human pose, occlusion, and self-occlusion. To solve this problem, a feature extraction method combining directional gradient of depth feature (DGoD) and local difference of depth feature (LDoD) is proposed in this paper, which uses a novel strategy that incorporates eight neighborhood points around a pixel for mutual comparison to calculate the difference between the pixels. A new data set is then established to train the random forest classifier, and a random forest two-way voting mechanism is adopted to classify the pixels on different parts of the human body depth image. Finally, the gravity center of each part is calculated and a reasonable point is selected as the joint to extract human skeleton. The experimental results show that the robustness and accuracy are significantly improved, associated with a competitive operating efficiency by evaluating our approach with the proposed data set.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:6271348

DOI: 10.1155/2018/6271348

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