Human Pose Recognition Based on Depth Image Multifeature Fusion
Joint Authors
Wang, Haikuan
Zhou, Feixiang
Zhou, Wenju
Chen, Ling
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-02
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Haikuan& Zhou, Feixiang& Zhou, Wenju& Chen, Ling. 2018. Human Pose Recognition Based on Depth Image Multifeature Fusion. Complexity،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1135034
Modern Language Association (MLA)
Wang, Haikuan…[et al.]. Human Pose Recognition Based on Depth Image Multifeature Fusion. Complexity No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1135034
American Medical Association (AMA)
Wang, Haikuan& Zhou, Feixiang& Zhou, Wenju& Chen, Ling. Human Pose Recognition Based on Depth Image Multifeature Fusion. Complexity. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1135034
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1135034