Zhang Yaolei, Zhang Fei, Zhou Yuanli, Xu Xiao
China Basketball College, Beijing Sport University, Beijing, China.
College of Physical Education, Hangzhou Normal University, Hangzhou, Zhejiang, China.
Front Neurorobot. 2024 Sep 25;18:1471327. doi: 10.3389/fnbot.2024.1471327. eCollection 2024.
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.
智能动作识别的进展有助于开发能够实时分析复杂人类活动的自主机器人系统，推动在动态环境中运行的机器人技术这一不断发展的领域。利用人工智能技术精确识别篮球运动员的动作，可以为运动员、教练和分析师提供有价值的帮助和指导，并有助于裁判在比赛中做出更公平的判罚。然而，与简单场景中的动作识别不同，篮球比赛中的背景相似且复杂，各种动作之间的差异细微，光照条件也不一致，这使得篮球比赛中的动作识别成为一项具有挑战性的任务。为了解决这个问题，本文提出了一种用于篮球运动员动作识别的自适应上下文感知网络（ACA-Net）。它包含一个长短期自适应（LSTA）模块和一个三元组空间通道交互（TSCI）模块，用于在时间、空间和通道层面提取有效特征。LSTA模块自适应地学习视频的全局和局部时间特征。TSCI模块通过学习空间和通道之间的交互特征来增强特征表示。我们在流行的篮球动作识别数据集SpaceJam和Basketball-51上进行了广泛的实验。结果表明，ACA-Net优于当前的主流方法，在两个数据集上的分类准确率分别达到了89.26%和92.05%。ACA-Net的自适应架构在自主机器人的实际应用中也具有潜力，在非结构化环境中准确识别复杂的人类动作对于自动比赛分析、球员表现评估和增强交互式广播体验等任务至关重要。
