ACA-Net: adaptive context-aware network for basketball action recognition
The advancements in intelligent action recognition can be instrumental in developing autonomous robotic systems capable of analyzing complex human activities in real-time, contributing to the growing field of robotics that operates in dynamic environments. The precise recognition of basketball playe...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-09-01
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| Series: | Frontiers in Neurorobotics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2024.1471327/full |
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| _version_ | 1850264156274425856 |
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| author | Yaolei Zhang Fei Zhang Yuanli Zhou Xiao Xu |
| author_facet | Yaolei Zhang Fei Zhang Yuanli Zhou Xiao Xu |
| author_sort | Yaolei Zhang |
| collection | DOAJ |
| description | The advancements in intelligent action recognition can be instrumental in developing autonomous robotic systems capable of analyzing complex human activities in real-time, contributing to the growing field of robotics that operates in dynamic environments. The precise recognition of basketball players' actions using artificial intelligence technology can provide valuable assistance and guidance to athletes, coaches, and analysts, and can help referees make fairer decisions during games. However, unlike action recognition in simpler scenarios, the background in basketball is similar and complex, the differences between various actions are subtle, and lighting conditions are inconsistent, making action recognition in basketball a challenging task. To address this problem, an Adaptive Context-Aware Network (ACA-Net) for basketball player action recognition is proposed in this paper. It contains a Long Short-term Adaptive (LSTA) module and a Triplet Spatial-Channel Interaction (TSCI) module to extract effective features at the temporal, spatial, and channel levels. The LSTA module adaptively learns global and local temporal features of the video. The TSCI module enhances the feature representation by learning the interaction features between space and channels. We conducted extensive experiments on the popular basketball action recognition datasets SpaceJam and Basketball-51. The results show that ACA-Net outperforms the current mainstream methods, achieving 89.26% and 92.05% in terms of classification accuracy on the two datasets, respectively. ACA-Net's adaptable architecture also holds potential for real-world applications in autonomous robotics, where accurate recognition of complex human actions in unstructured environments is crucial for tasks such as automated game analysis, player performance evaluation, and enhanced interactive broadcasting experiences. |
| format | Article |
| id | doaj-art-3b8fffe39af84f96903d4cea079e1171 |
| institution | OA Journals |
| issn | 1662-5218 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj-art-3b8fffe39af84f96903d4cea079e11712025-08-20T01:54:46ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182024-09-011810.3389/fnbot.2024.14713271471327ACA-Net: adaptive context-aware network for basketball action recognitionYaolei Zhang0Fei Zhang1Yuanli Zhou2Xiao Xu3China Basketball College, Beijing Sport University, Beijing, ChinaCollege of Physical Education, Hangzhou Normal University, Hangzhou, Zhejiang, ChinaRadar Non-Commissioned Officers' School, Air Force Early Warning Academy, Wuhan, Hubei, ChinaCollege of Physical Education, Dalian University, Dalian, Liaoning, ChinaThe advancements in intelligent action recognition can be instrumental in developing autonomous robotic systems capable of analyzing complex human activities in real-time, contributing to the growing field of robotics that operates in dynamic environments. The precise recognition of basketball players' actions using artificial intelligence technology can provide valuable assistance and guidance to athletes, coaches, and analysts, and can help referees make fairer decisions during games. However, unlike action recognition in simpler scenarios, the background in basketball is similar and complex, the differences between various actions are subtle, and lighting conditions are inconsistent, making action recognition in basketball a challenging task. To address this problem, an Adaptive Context-Aware Network (ACA-Net) for basketball player action recognition is proposed in this paper. It contains a Long Short-term Adaptive (LSTA) module and a Triplet Spatial-Channel Interaction (TSCI) module to extract effective features at the temporal, spatial, and channel levels. The LSTA module adaptively learns global and local temporal features of the video. The TSCI module enhances the feature representation by learning the interaction features between space and channels. We conducted extensive experiments on the popular basketball action recognition datasets SpaceJam and Basketball-51. The results show that ACA-Net outperforms the current mainstream methods, achieving 89.26% and 92.05% in terms of classification accuracy on the two datasets, respectively. ACA-Net's adaptable architecture also holds potential for real-world applications in autonomous robotics, where accurate recognition of complex human actions in unstructured environments is crucial for tasks such as automated game analysis, player performance evaluation, and enhanced interactive broadcasting experiences.https://www.frontiersin.org/articles/10.3389/fnbot.2024.1471327/fullbasketballaction recognitionadaptive context-awarenesslong short-term informationspace-channel information interaction |
| spellingShingle | Yaolei Zhang Fei Zhang Yuanli Zhou Xiao Xu ACA-Net: adaptive context-aware network for basketball action recognition Frontiers in Neurorobotics basketball action recognition adaptive context-awareness long short-term information space-channel information interaction |
| title | ACA-Net: adaptive context-aware network for basketball action recognition |
| title_full | ACA-Net: adaptive context-aware network for basketball action recognition |
| title_fullStr | ACA-Net: adaptive context-aware network for basketball action recognition |
| title_full_unstemmed | ACA-Net: adaptive context-aware network for basketball action recognition |
| title_short | ACA-Net: adaptive context-aware network for basketball action recognition |
| title_sort | aca net adaptive context aware network for basketball action recognition |
| topic | basketball action recognition adaptive context-awareness long short-term information space-channel information interaction |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2024.1471327/full |
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