Jiang Li, Lu Wang
School of Physical Education of Yantai University, Yantai, China.
Front Neurorobot. 2023 Oct 30;17:1275645. doi: 10.3389/fnbot.2023.1275645. eCollection 2023.
This paper presents an innovative Intelligent Robot Sports Competition Tactical Analysis Model that leverages multimodal perception to tackle the pressing challenge of analyzing opponent tactics in sports competitions. The current landscape of sports competition analysis necessitates a comprehensive understanding of opponent strategies. However, traditional methods are often constrained to a single data source or modality, limiting their ability to capture the intricate details of opponent tactics.
Our system integrates the Swin Transformer and CLIP models, harnessing cross-modal transfer learning to enable a holistic observation and analysis of opponent tactics. The Swin Transformer is employed to acquire knowledge about opponent action postures and behavioral patterns in basketball or football games, while the CLIP model enhances the system's comprehension of opponent tactical information by establishing semantic associations between images and text. To address potential imbalances and biases between these models, we introduce a cross-modal transfer learning technique that mitigates modal bias issues, thereby enhancing the model's generalization performance on multimodal data.
Through cross-modal transfer learning, tactical information learned from images by the Swin Transformer is effectively transferred to the CLIP model, providing coaches and athletes with comprehensive tactical insights. Our method is rigorously tested and validated using Sport UV, Sports-1M, HMDB51, and NPU RGB+D datasets. Experimental results demonstrate the system's impressive performance in terms of prediction accuracy, stability, training time, inference time, number of parameters, and computational complexity. Notably, the system outperforms other models, with a remarkable 8.47% lower prediction error (MAE) on the Kinetics dataset, accompanied by a 72.86-second reduction in training time.
The presented system proves to be highly suitable for real-time sports competition assistance and analysis, offering a novel and effective approach for an Intelligent Robot Sports Competition Tactical Analysis Model that maximizes the potential of multimodal perception technology. By harnessing the synergies between the Swin Transformer and CLIP models, we address the limitations of traditional methods and significantly advance the field of sports competition analysis. This innovative model opens up new avenues for comprehensive tactical analysis in sports, benefiting coaches, athletes, and sports enthusiasts alike.
本文提出了一种创新的智能机器人体育竞赛战术分析模型，该模型利用多模态感知来应对体育竞赛中分析对手战术这一紧迫挑战。当前体育竞赛分析的现状需要全面了解对手策略。然而，传统方法往往局限于单一数据源或模态，限制了它们捕捉对手战术复杂细节的能力。
我们的系统集成了Swin Transformer和CLIP模型，利用跨模态迁移学习对对手战术进行整体观察和分析。Swin Transformer用于获取篮球或足球比赛中对手动作姿态和行为模式的知识，而CLIP模型通过在图像和文本之间建立语义关联来增强系统对对手战术信息的理解。为了解决这些模型之间潜在的不平衡和偏差问题，我们引入了一种跨模态迁移学习技术，减轻模态偏差问题，从而提高模型在多模态数据上的泛化性能。
通过跨模态迁移学习，Swin Transformer从图像中学习到的战术信息被有效地转移到CLIP模型中，为教练和运动员提供了全面的战术见解。我们的方法使用Sport UV、Sports-1M、HMDB51和NPU RGB+D数据集进行了严格测试和验证。实验结果表明，该系统在预测准确性、稳定性、训练时间、推理时间、参数数量和计算复杂度方面表现出色。值得注意的是，该系统优于其他模型，在Kinetics数据集上预测误差（平均绝对误差）显著降低8.47%，同时训练时间减少了72.86秒。
所提出的系统被证明非常适合实时体育竞赛辅助和分析，为智能机器人体育竞赛战术分析模型提供了一种新颖有效的方法，最大限度地发挥了多模态感知技术的潜力。通过利用Swin Transformer和CLIP模型之间的协同作用，我们解决了传统方法的局限性，并显著推进了体育竞赛分析领域。这种创新模型为体育领域的全面战术分析开辟了新途径，使教练、运动员和体育爱好者都受益。
