用直播画面追踪球员位置速度，精度可达米级
Concurrent validity of computer-vision artificial intelligence player tracking software using broadcast footage
- 用商业视觉算法分析直播画面定位球员坐标与速度
- 位置误差1.68~16.39米，速度误差0.34~2.38米/秒
- 战术视角+高清画质可提升追踪准确率，适合体育数据分析
本研究旨在评估商用计算机视觉与人工智能（AI）球员追踪软件在使用直播画面时，对球员位置、速度和总跑动距离的测量准确性，并考察摄像机视角与分辨率对精度的影响。数据来自2022年卡塔尔国际足联世界杯一场比赛，采用战术、节目及摄像机1号三种信号源。三家使用计算机视觉与AI的追踪服务提供商分别分析了每位球员的瞬时位置（x, y坐标）与速度（m·s⁻¹）。其结果与高精度多相机追踪系统（TRACAB Gen 5）进行对比，计算均方根误差（RMSE）与均值偏差。位置RMSE为1.68至16.39米，速度RMSE为0.34至2.38米/秒，全场总跑动距离的均值偏差范围为-1745米（-21.8%）至1945米（24.3%）。结果表明，当球员被软件成功检测时，计算机视觉与AI追踪具备较合理精度。建议追踪位置与速度时优先使用战术视角，以提高球员识别率，从而提升准确性；720p与1080p分辨率均可适用，前提是采用合适的计算机视觉与AI模型。
原文摘要 · Abstract (English)
This study aimed to: (1) understand whether commercially available computer-vision and artificial intelligence (AI) player tracking software can accurately measure player position, speed and distance using broadcast footage and (2) determine the impact of camera feed and resolution on accuracy. Data were obtained from one match at the 2022 Qatar Federation Internationale de Football Association (FIFA) World Cup. Tactical, programme and camera 1 feeds were used. Three commercial tracking providers that use computer-vision and AI participated. Providers analysed instantaneous position (x, y coordinates) and speed (m\,s^{-1}) of each player. Their data were compared with a high-definition multi-camera tracking system (TRACAB Gen 5). Root mean square error (RMSE) and mean bias were calculated. Position RMSE ranged from 1.68 to 16.39 m, while speed RMSE ranged from 0.34 to 2.38 m\,s^{-1}. Total match distance mean bias ranged from -1745 m (-21.8%) to 1945 m (24.3%) across providers. Computer-vision and AI player tracking software offer the ability to track players with fair precision when players are detected by the software. Providers should use a tactical feed when tracking position and speed, which will maximise player detection, improving accuracy. Both 720p and 1080p resolutions are suitable, assuming appropriate computer-vision and AI models are implemented.
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