Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8717942 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832565970116280320 |
|---|---|
| author | Qiubo Zhong Caiming Zheng Haoxiang Zhang |
| author_facet | Qiubo Zhong Caiming Zheng Haoxiang Zhang |
| author_sort | Qiubo Zhong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-038c567dffc342738ed364cb12bd4303 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-038c567dffc342738ed364cb12bd43032025-02-03T01:05:22ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/87179428717942Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot InteractionQiubo Zhong0Caiming Zheng1Haoxiang Zhang2Robotics Institute, Ningbo University of Technology, Ningbo 315211, ChinaRobotics Institute, Ningbo University of Technology, Ningbo 315211, ChinaRobotics Institute, Ningbo University of Technology, Ningbo 315211, ChinaA 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.http://dx.doi.org/10.1155/2020/8717942 |
| spellingShingle | Qiubo Zhong Caiming Zheng Haoxiang Zhang Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction Complexity |
| title | Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction |
| title_full | Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction |
| title_fullStr | Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction |
| title_full_unstemmed | Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction |
| title_short | Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction |
| title_sort | research on discriminative skeleton based action recognition in spatiotemporal fusion and human robot interaction |
| url | http://dx.doi.org/10.1155/2020/8717942 |
| work_keys_str_mv | AT qiubozhong researchondiscriminativeskeletonbasedactionrecognitioninspatiotemporalfusionandhumanrobotinteraction AT caimingzheng researchondiscriminativeskeletonbasedactionrecognitioninspatiotemporalfusionandhumanrobotinteraction AT haoxiangzhang researchondiscriminativeskeletonbasedactionrecognitioninspatiotemporalfusionandhumanrobotinteraction |