Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction
Joint Authors
Zhong, Qiubo
Zheng, Caiming
Zhang, Haoxiang
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-25
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
A novel posture motion-based spatiotemporal fused graph convolutional network (PM-STGCN) is presented for skeleton-based action recognition.
Existing methods on skeleton-based action recognition focus on independently calculating the joint information in single frame and motion information of joints between adjacent frames from the human body skeleton structure and then combine the classification results.
However, that does not take into consideration of the complicated temporal and spatial relationship of the human body action sequence, so they are not very efficient in distinguishing similar actions.
In this work, we enhance the ability of distinguishing similar actions by focusing on spatiotemporal fusion and adaptive feature extraction for high discrimination information.
Firstly, the local posture motion-based attention (LPM-TAM) module is proposed for the purpose of suppressing the skeleton sequence data with a low amount of motion in the temporal domain, and the representation of motion posture features is concentrated.
Besides, the local posture motion-based channel attention module (LPM-CAM) is introduced to make use of the strongly discriminative representation between different action classes of similarity.
Finally, the posture motion-based spatiotemporal fusion (PM-STF) module is constructed which fuses the spatiotemporal skeleton data by filtering out the low-information sequence and enhances the posture motion features adaptively with high discrimination.
Extensive experiments have been conducted, and the results demonstrate that the proposed model is superior to the commonly used action recognition methods.
The designed human-robot interaction system based on action recognition has competitive performance compared with the speech interaction system.
American Psychological Association (APA)
Zhong, Qiubo& Zheng, Caiming& Zhang, Haoxiang. 2020. Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction. Complexity،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1144523
Modern Language Association (MLA)
Zhong, Qiubo…[et al.]. Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction. Complexity No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1144523
American Medical Association (AMA)
Zhong, Qiubo& Zheng, Caiming& Zhang, Haoxiang. Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction. Complexity. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1144523
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1144523