Abstract
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.
Abstract (translated)
我们提出了一种全面的基于视频的网球双打分析框架，旨在解决该战略复杂的运动缺乏自动化分析工具的问题。我们的方法引入了标准化注释方法，涵盖了球员位置、击球类型、场地布局和比赛结果，并结合了一个专门设计以满足网球视频标注独特需求的注释工具。 该框架集成了先进的机器学习技术，包括通过自然语言定位实现精确球员定位的GroundingDINO以及用于稳健姿态估计的YOLO-Pose。这种组合显著减少了手动注释的工作量，同时提高了数据的一致性和质量。我们在双打网球比赛的数据上评估了我们的方法，并展示了基于卷积神经网络（CNN）并通过迁移学习的方法在预测击球类型、球员位置和场地布局方面远胜过姿态基线方法。 这些CNN模型能够有效捕捉对双打网球分析至关重要的复杂视觉和上下文特征。我们集成的系统将高级分析能力与网球双打的战略复杂性相结合，为职业网球中的战术分析、性能评估和战略建模提供了基础。
URL
https://arxiv.org/abs/2507.02906
