Single-Stage Pose Estimation and Joint Angle Extraction Method for Moving Human Body
Detecting posture changes of athletes in sports is an important task in teaching and training competitions, but its detection remains challenging due to the diversity and complexity of sports postures. This paper introduces a single-stage pose estimation algorithm named yolov8-sp. This algorithm enh...
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| Format: | Article |
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MDPI AG
2023-11-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/22/4644 |
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| author | Shuxian Wang Xiaoxun Zhang Fang Ma Jiaming Li Yuanyou Huang |
| author_facet | Shuxian Wang Xiaoxun Zhang Fang Ma Jiaming Li Yuanyou Huang |
| author_sort | Shuxian Wang |
| collection | DOAJ |
| description | Detecting posture changes of athletes in sports is an important task in teaching and training competitions, but its detection remains challenging due to the diversity and complexity of sports postures. This paper introduces a single-stage pose estimation algorithm named yolov8-sp. This algorithm enhances the original yolov8 architecture by incorporating the concept of multi-dimensional feature fusion and the attention mechanism for automatically capturing feature importance. Furthermore, in this paper, angle extraction is conducted for three crucial motion joints in the motion scene, with polynomial corrections applied across successive frames. In comparison with the baseline yolov8, the improved model significantly outperforms it in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mn>50</mn></mrow></msup></mrow></semantics></math></inline-formula> (average precision) aspects. Specifically, the model’s performance improves from 84.5 AP to 87.1 AP, and the performance of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mn>50</mn><mtext>–</mtext><mn>95</mn></mrow></msup></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mi>M</mi></mrow></msup></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mi>L</mi></mrow></msup></mrow></semantics></math></inline-formula> aspects also shows varying degrees of improvement; the joint angle detection accuracy under different sports scenarios is tested, and the overall accuracy is improved from 73.2% to 89.0%, which proves the feasibility of the method for posture estimation of the human body in sports and provides a reliable tool for the analysis of athletes’ joint angles. |
| first_indexed | 2024-03-09T16:52:29Z |
| format | Article |
| id | doaj.art-1b1d75592912436ca61ee46ef667046d |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T16:52:29Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-1b1d75592912436ca61ee46ef667046d2023-11-24T14:39:27ZengMDPI AGElectronics2079-92922023-11-011222464410.3390/electronics12224644Single-Stage Pose Estimation and Joint Angle Extraction Method for Moving Human BodyShuxian Wang0Xiaoxun Zhang1Fang Ma2Jiaming Li3Yuanyou Huang4School of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaDetecting posture changes of athletes in sports is an important task in teaching and training competitions, but its detection remains challenging due to the diversity and complexity of sports postures. This paper introduces a single-stage pose estimation algorithm named yolov8-sp. This algorithm enhances the original yolov8 architecture by incorporating the concept of multi-dimensional feature fusion and the attention mechanism for automatically capturing feature importance. Furthermore, in this paper, angle extraction is conducted for three crucial motion joints in the motion scene, with polynomial corrections applied across successive frames. In comparison with the baseline yolov8, the improved model significantly outperforms it in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mn>50</mn></mrow></msup></mrow></semantics></math></inline-formula> (average precision) aspects. Specifically, the model’s performance improves from 84.5 AP to 87.1 AP, and the performance of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mn>50</mn><mtext>–</mtext><mn>95</mn></mrow></msup></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mi>M</mi></mrow></msup></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mi>L</mi></mrow></msup></mrow></semantics></math></inline-formula> aspects also shows varying degrees of improvement; the joint angle detection accuracy under different sports scenarios is tested, and the overall accuracy is improved from 73.2% to 89.0%, which proves the feasibility of the method for posture estimation of the human body in sports and provides a reliable tool for the analysis of athletes’ joint angles.https://www.mdpi.com/2079-9292/12/22/4644pose estimationyolov8-spjoint angle extractionfeature extractionmulti-dimensional feature fusion |
| spellingShingle | Shuxian Wang Xiaoxun Zhang Fang Ma Jiaming Li Yuanyou Huang Single-Stage Pose Estimation and Joint Angle Extraction Method for Moving Human Body Electronics pose estimation yolov8-sp joint angle extraction feature extraction multi-dimensional feature fusion |
| title | Single-Stage Pose Estimation and Joint Angle Extraction Method for Moving Human Body |
| title_full | Single-Stage Pose Estimation and Joint Angle Extraction Method for Moving Human Body |
| title_fullStr | Single-Stage Pose Estimation and Joint Angle Extraction Method for Moving Human Body |
| title_full_unstemmed | Single-Stage Pose Estimation and Joint Angle Extraction Method for Moving Human Body |
| title_short | Single-Stage Pose Estimation and Joint Angle Extraction Method for Moving Human Body |
| title_sort | single stage pose estimation and joint angle extraction method for moving human body |
| topic | pose estimation yolov8-sp joint angle extraction feature extraction multi-dimensional feature fusion |
| url | https://www.mdpi.com/2079-9292/12/22/4644 |
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