用AI实时追踪网球选手与球，自动分析比赛数据。
Automated Tennis Player and Ball Tracking with Court Keypoints Detection (Hawk Eye System)
- 融合YOLOv8、自训练YOLOv5和ResNet50，实现多目标追踪。
- 可检测球员动作、球速、击球准确率与反应时间等指标。
- 适合教练、转播方和球员用于比赛复盘与战术优化。
本研究提出一套完整的网球比赛自动化分析流程。系统集成多个深度学习模型，实现实时检测与追踪球员及网球，并识别球场关键点以提供空间参考。采用YOLOv8进行球员检测，使用自训练的YOLOv5模型追踪网球，基于ResNet50架构识别球场关键点。系统输出包含标注视频与详细性能指标，涵盖球员移动模式、球速、击球准确率及反应时间等。实验表明，在不同场地条件与比赛场景下均表现稳健。结果可用于教练、转播方与运动员获取比赛动态的可操作洞察。
原文摘要 · Abstract (English)
This study presents a complete pipeline for automated tennis match analysis. Our framework integrates multiple deep learning models to detect and track players and the tennis ball in real time, while also identifying court keypoints for spatial reference. Using YOLOv8 for player detection, a custom-trained YOLOv5 model for ball tracking, and a ResNet50-based architecture for court keypoint detection, our system provides detailed analytics including player movement patterns, ball speed, shot accuracy, and player reaction times. The experimental results demonstrate robust performance in varying court conditions and match scenarios. The model outputs an annotated video along with detailed performance metrics, enabling coaches, broadcasters, and players to gain actionable insights into the dynamics of the game.
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