🤖 AI Summary
Commercial AI-based player tracking systems lack systematic validation for concurrent validity in broadcast video, particularly regarding positional accuracy, velocity estimation, and total distance covered during elite football matches. Method: This study conducts the first comprehensive concurrent validity assessment of three leading commercial AI tracking solutions on FIFA World Cup broadcast footage, using multi-camera, high-definition TRACAB Gen 5 data as the ground-truth reference. It quantifies errors in position (RMSE), speed, and cumulative running distance, while analyzing the impact of camera viewpoint (e.g.,俯角, wide-angle) and resolution. Results: Positional RMSE ranges from 1.68–16.39 m; speed error from 0.34–2.38 m/s; and total distance bias from −21.8% to +24.3%. Crucially, tactical camera angles—especially elevated and wide-field views—significantly improve localization accuracy, demonstrating that broadcast geometry fundamentally constrains AI tracking performance. This work establishes the first empirical benchmark and methodological framework for validating AI-driven sports analytics in live broadcast environments.
📝 Abstract
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.