DSPose: Dual-Space-Driven Keypoint Topology Modeling for Human Pose Estimation
Human pose estimation is the basis of many downstream tasks, such as motor intervention, behavior understanding, and human–computer interaction. The existing human pose estimation methods rely too much on the similarity of keypoints at the image feature level, which is vulnerable to three problems:...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/17/7626 |
| _version_ | 1797581877705441280 |
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| author | Anran Zhao Jingli Li Hongtao Zeng Hongren Cheng Liangshan Dong |
| author_facet | Anran Zhao Jingli Li Hongtao Zeng Hongren Cheng Liangshan Dong |
| author_sort | Anran Zhao |
| collection | DOAJ |
| description | Human pose estimation is the basis of many downstream tasks, such as motor intervention, behavior understanding, and human–computer interaction. The existing human pose estimation methods rely too much on the similarity of keypoints at the image feature level, which is vulnerable to three problems: object occlusion, keypoints ghost, and neighbor pose interference. We propose a dual-space-driven topology model for the human pose estimation task. Firstly, the model extracts relatively accurate keypoints features through a Transformer-based feature extraction method. Then, the correlation of keypoints in the physical space is introduced to alleviate the error localization problem caused by excessive dependence on the feature-level representation of the model. Finally, through the graph convolutional neural network, the spatial correlation of keypoints and the feature correlation are effectively fused to obtain more accurate human pose estimation results. The experimental results on real datasets also further verify the effectiveness of our proposed model. |
| first_indexed | 2024-03-10T23:12:50Z |
| format | Article |
| id | doaj.art-ded326543e6146cbb0c0022f750c14ed |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:12:50Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ded326543e6146cbb0c0022f750c14ed2023-11-19T08:52:24ZengMDPI AGSensors1424-82202023-09-012317762610.3390/s23177626DSPose: Dual-Space-Driven Keypoint Topology Modeling for Human Pose EstimationAnran Zhao0Jingli Li1Hongtao Zeng2Hongren Cheng3Liangshan Dong4School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Physical Education, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Physical Education, Huazhong University of Science and Technology, Wuhan 430074, ChinaSports Big-Data Research Center, Wuhan Sports University, Wuhan 430079, ChinaSchool of Physical Education, China University of Geosciences, Wuhan 430074, ChinaHuman pose estimation is the basis of many downstream tasks, such as motor intervention, behavior understanding, and human–computer interaction. The existing human pose estimation methods rely too much on the similarity of keypoints at the image feature level, which is vulnerable to three problems: object occlusion, keypoints ghost, and neighbor pose interference. We propose a dual-space-driven topology model for the human pose estimation task. Firstly, the model extracts relatively accurate keypoints features through a Transformer-based feature extraction method. Then, the correlation of keypoints in the physical space is introduced to alleviate the error localization problem caused by excessive dependence on the feature-level representation of the model. Finally, through the graph convolutional neural network, the spatial correlation of keypoints and the feature correlation are effectively fused to obtain more accurate human pose estimation results. The experimental results on real datasets also further verify the effectiveness of our proposed model.https://www.mdpi.com/1424-8220/23/17/7626human pose estimationTransformergraph convolutional networkdual spacekeypoint detection |
| spellingShingle | Anran Zhao Jingli Li Hongtao Zeng Hongren Cheng Liangshan Dong DSPose: Dual-Space-Driven Keypoint Topology Modeling for Human Pose Estimation Sensors human pose estimation Transformer graph convolutional network dual space keypoint detection |
| title | DSPose: Dual-Space-Driven Keypoint Topology Modeling for Human Pose Estimation |
| title_full | DSPose: Dual-Space-Driven Keypoint Topology Modeling for Human Pose Estimation |
| title_fullStr | DSPose: Dual-Space-Driven Keypoint Topology Modeling for Human Pose Estimation |
| title_full_unstemmed | DSPose: Dual-Space-Driven Keypoint Topology Modeling for Human Pose Estimation |
| title_short | DSPose: Dual-Space-Driven Keypoint Topology Modeling for Human Pose Estimation |
| title_sort | dspose dual space driven keypoint topology modeling for human pose estimation |
| topic | human pose estimation Transformer graph convolutional network dual space keypoint detection |
| url | https://www.mdpi.com/1424-8220/23/17/7626 |
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