用智能视频分析自动解析网球双打战术，提升训练与比赛策略效率
Enhancing Sports Strategy with Video Analytics and Data Mining: Automated Video-Based Analytics Framework for Tennis Doubles
- 结合语言定位与姿态估计，实现球员位置与击球类型的精准标注
- 迁移学习的CNN模型在击球类型等预测上显著优于传统姿态方法
- 适合职业网球教练、数据分析团队用于战术复盘与选手评估
我们提出一个面向网球双打的综合性视频分析框架，填补该战略复杂运动缺乏自动化分析工具的空白。框架采用标准化标注方法，涵盖球员位置、击球类型、场地阵型及比赛结果，并配备专用标注工具以满足网球视频标注需求。整合GroundingDINO实现基于自然语言的精准球员定位，YOLO-Pose完成鲁棒姿态估计，大幅降低人工标注成本并提升数据一致性与质量。在双打比赛数据上的实验表明，采用迁移学习的卷积神经网络（CNN）模型在击球类型、球员位置和阵型预测任务中显著优于基于姿态的方法，能有效捕捉双打比赛中复杂的视觉与上下文特征。该系统将先进分析能力与双打战略复杂性相结合，为职业网球中的自动化战术分析、表现评估与战略建模提供基础。
原文摘要 · Abstract (English)
We present a comprehensive video-based analytics framework for tennis doubles that addresses the lack of automated analysis tools for this strategically complex sport. Our approach introduces a standardised annotation methodology encompassing player positioning, shot types, court formations, and match outcomes, coupled with a specialised annotation tool designed to meet the unique requirements of tennis video labelling. The framework integrates advanced machine learning techniques including GroundingDINO for precise player localisation through natural language grounding and YOLO-Pose for robust pose estimation. This combination significantly reduces manual annotation effort whilst improving data consistency and quality. We evaluate our approach on doubles tennis match data and demonstrate that CNN-based models with transfer learning substantially outperform pose-based methods for predicting shot types, player positioning, and formations. The CNN models effectively capture complex visual and contextual features essential for doubles tennis analysis. Our integrated system bridges advanced analytical capabilities with the strategic complexities of tennis doubles, providing a foundation for automated tactical analysis, performance evaluation, and strategic modelling in professional tennis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。