🤖 AI Summary
This study addresses the challenge faced by low-budget football teams in accessing professional-grade player and match analytics due to the high cost of multi-camera setups or GPS tracking systems. To overcome this barrier, the authors propose an end-to-end single-camera visual system that, for the first time, enables joint detection and tracking of players, referees, goalkeepers, and the ball directly from a single broadcast video stream. The system leverages a YOLO-based object detector integrated with the ByteTrack multi-object tracking algorithm to form a lightweight computer vision pipeline. Experimental results demonstrate high precision, recall, and strong mAP50 performance in tracking players and referees, confirming the feasibility of efficiently extracting spatial motion data from standard broadcast footage and substantially lowering the entry threshold for football performance analysis.
📝 Abstract
Clubs with access to expensive multi-camera setups or GPS tracking systems gain a competitive advantage through detailed data, whereas lower-budget teams are often unable to collect similar information. This paper examines whether such data can instead be extracted directly from standard broadcast footage using a single-camera computer vision pipeline. This project develops an end-to-end system that combines a YOLO object detector with the ByteTrack tracking algorithm to identify and track players, referees, goalkeepers, and the ball throughout a match. Experimental results show that the pipeline achieves high performance in detecting and tracking players and officials, with strong precision, recall, and mAP50 scores, while ball detection remains the primary challenge. Despite this limitation, our findings demonstrate that AI can extract meaningful player-level spatial information from a single broadcast camera. By reducing reliance on specialized hardware, the proposed approach enables colleges, academies, and amateur clubs to adopt scalable, data-driven analysis methods previously accessible only to professional teams, highlighting the potential for affordable computer vision-based soccer analytics.