Enhancing accuracy in dynamic pose estimation for sports competitions using HRPose: A hybrid approach integrating SinglePose AI
Human pose estimation plays a critical role in various applications, such as sports performance evaluation, rehabilitation, and human–computer interaction. Recent advancements in deep learning have significantly improved the accuracy and robustness of human pose estimation models. However, challenge...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825005587 |
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| _version_ | 1849229304292966400 |
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| author | Rui Han Mingnong Yi Wei Feng Feng Qi Yining Zhou |
| author_facet | Rui Han Mingnong Yi Wei Feng Feng Qi Yining Zhou |
| author_sort | Rui Han |
| collection | DOAJ |
| description | Human pose estimation plays a critical role in various applications, such as sports performance evaluation, rehabilitation, and human–computer interaction. Recent advancements in deep learning have significantly improved the accuracy and robustness of human pose estimation models. However, challenges remain in dynamic environments, especially in sports competitions, where high-speed movements, occlusions, and complex backgrounds often hinder accurate estimation. This paper proposes HRPose, a novel approach that combines HRNet for feature extraction and SinglePose AI for precise keypoint localization. It maintains high-resolution feature maps throughout the feature extraction process, enabling the model to capture fine-grained spatial details. SinglePose AI uses these features to generate and refine keypoint heatmaps, achieving accurate pose estimation even in challenging conditions. We evaluate HRPose on benchmark datasets, including the MPII Human Pose and PoseTrack datasets, and compare it with several models. Our results demonstrate that HRPose achieves superior performance in terms of mAP, precision, and robustness. Additionally, we discuss the real-time performance of HRPose and its potential applications in various domains, such as sports, healthcare, and rehabilitation. Future work will focus on improving the model’s robustness to extreme conditions, such as low lighting and motion blur, and exploring its integration with multimodal data for more comprehensive analysis. |
| format | Article |
| id | doaj-art-533ac7e2fe9a4d6a9ceccb6a0a8f9370 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-533ac7e2fe9a4d6a9ceccb6a0a8f93702025-08-22T04:55:14ZengElsevierAlexandria Engineering Journal1110-01682025-08-0112720021310.1016/j.aej.2025.04.062Enhancing accuracy in dynamic pose estimation for sports competitions using HRPose: A hybrid approach integrating SinglePose AIRui Han0Mingnong Yi1Wei Feng2Feng Qi3Yining Zhou4Key Laboratory of Sports Engineering of General Administration of Sport of China, Wuhan Sports University, 430079, Wuhan, Hubei, ChinaKey Laboratory of Sports Engineering of General Administration of Sport of China, Wuhan Sports University, 430079, Wuhan, Hubei, China; Corresponding author.School of Artificial Intelligence and Automation Huazhong University of Science and Technology(HUST), 430074, Wuhan, Hubei, ChinaEngineering Research Center of Sports Health Intelligent Equipment of Hubei Province, Wuhan Sports University, 430079, Wuhan, Hubei, China; Research Center of Sports Equipment Engineering Technology of Hubei Province, Wuhan Sports University, 430079, Wuhan, Hubei, ChinaTexas A&M University Newark, CA, United States of AmericaHuman pose estimation plays a critical role in various applications, such as sports performance evaluation, rehabilitation, and human–computer interaction. Recent advancements in deep learning have significantly improved the accuracy and robustness of human pose estimation models. However, challenges remain in dynamic environments, especially in sports competitions, where high-speed movements, occlusions, and complex backgrounds often hinder accurate estimation. This paper proposes HRPose, a novel approach that combines HRNet for feature extraction and SinglePose AI for precise keypoint localization. It maintains high-resolution feature maps throughout the feature extraction process, enabling the model to capture fine-grained spatial details. SinglePose AI uses these features to generate and refine keypoint heatmaps, achieving accurate pose estimation even in challenging conditions. We evaluate HRPose on benchmark datasets, including the MPII Human Pose and PoseTrack datasets, and compare it with several models. Our results demonstrate that HRPose achieves superior performance in terms of mAP, precision, and robustness. Additionally, we discuss the real-time performance of HRPose and its potential applications in various domains, such as sports, healthcare, and rehabilitation. Future work will focus on improving the model’s robustness to extreme conditions, such as low lighting and motion blur, and exploring its integration with multimodal data for more comprehensive analysis.http://www.sciencedirect.com/science/article/pii/S1110016825005587Human pose estimationSports analysisReal-time processingKeypoint localizationDeep learningSinglePose AI |
| spellingShingle | Rui Han Mingnong Yi Wei Feng Feng Qi Yining Zhou Enhancing accuracy in dynamic pose estimation for sports competitions using HRPose: A hybrid approach integrating SinglePose AI Alexandria Engineering Journal Human pose estimation Sports analysis Real-time processing Keypoint localization Deep learning SinglePose AI |
| title | Enhancing accuracy in dynamic pose estimation for sports competitions using HRPose: A hybrid approach integrating SinglePose AI |
| title_full | Enhancing accuracy in dynamic pose estimation for sports competitions using HRPose: A hybrid approach integrating SinglePose AI |
| title_fullStr | Enhancing accuracy in dynamic pose estimation for sports competitions using HRPose: A hybrid approach integrating SinglePose AI |
| title_full_unstemmed | Enhancing accuracy in dynamic pose estimation for sports competitions using HRPose: A hybrid approach integrating SinglePose AI |
| title_short | Enhancing accuracy in dynamic pose estimation for sports competitions using HRPose: A hybrid approach integrating SinglePose AI |
| title_sort | enhancing accuracy in dynamic pose estimation for sports competitions using hrpose a hybrid approach integrating singlepose ai |
| topic | Human pose estimation Sports analysis Real-time processing Keypoint localization Deep learning SinglePose AI |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825005587 |
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