Deep learning-based detection of players and teams in soccer videos with positioning heatmap generation

Abstract

Purpose: This paper uses computer vision to develop a method for collecting football players’ positions during a match. It also compares the differences between various solutions of the existing object detection models to determine discerning differences. Then, an illustration of the obtained information is created as a heatmap. Design/methodology/approach: The proposed method utilizes a dataset of side-view football broadcast footage to detect the players’ positions on the football at any given moment of the football match. Using YOLO object detection, the players of each team are identified and recorded to create valuable illustrations that the coaches can further analyze, training staff, etc. Using three different implementations of YOLO (YOLOv5m6, YOLOv5l-tph and YOLOv5l-tph-plus) the results will be compared to find out the best implementation for this specific task. Findings: After each YOLO implementation was trained using the same dataset, the results showed that YOLOv5l-tph performed best, achieving a precision of 0.9868. Meanwhile, YOLOv5l-tph-plus placed second, with a precision of 0.9786. Finally, YOLOv5m6 performed worst, with a precision of 0.8214. Originality/value: In sports, various analytical information can be extracted from games, whether from statistical records or recorded footage. For “sports analytics,” the goal is to provide valuable insights that are otherwise not obtainable through traditional means, such as merely rewatching past footage. In both the past and the present, there have been multiple attempts at using statistical records to predict or quantify various aspects of the games.