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ORIGINAL RESEARCH article

Front. Neurorobot.
Volume 18 - 2024 | doi: 10.3389/fnbot.2024.1471327
This article is part of the Research Topic Neural Network Models in Autonomous Robotics View all articles

ACA-Net: Adaptive Context-Aware Network for Basketball Action Recognition

Provisionally accepted
Yaolei Zhang Yaolei Zhang 1*Fei Zhang Fei Zhang 2Yuanli Zhou Yuanli Zhou 3Xiao Xu Xiao Xu 4*
  • 1 China Basketball College, Beijing Sport University, Beijing, China
  • 2 School of Physical Education, Hangzhou Normal University, Hangzhou, Zhejiang Province, China
  • 3 Air Force Early Warning Academy, Ministry of National Defense of the People's Republic of China, Wuhan, Hebei Province, China
  • 4 Dalian University College of Physical Education, Dalian, Liaoning Province, China

The final, formatted version of the article will be published soon.

    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.

    Keywords: Baketball, Action recognition, adaptive context-awareness, Long short-term information, space-channel information interaction

    Received: 27 Jul 2024; Accepted: 09 Sep 2024.

    Copyright: © 2024 Zhang, Zhang, Zhou and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yaolei Zhang, China Basketball College, Beijing Sport University, Beijing, China
    Xiao Xu, Dalian University College of Physical Education, Dalian, 130012, Liaoning Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.