Setting a Baseline for long-shot real-time Player and Ball detection in Soccer Videos
This work provides a benchmark for soccer analytics, though it is incremental as it builds on existing datasets and models.
The authors tackled the problem of player and ball detection in soccer videos by creating a new dataset from SoccerNet v3 and demonstrating that YOLO8n outperforms FootAndBall in long-shot real-time detection.
Players and ball detection are among the first required steps on a football analytics platform. Until recently, the existing open datasets on which the evaluations of most models were based, were not sufficient. In this work, we point out their weaknesses, and with the advent of the SoccerNet v3, we propose and deliver to the community an edited part of its dataset, in YOLO normalized annotation format for training and evaluation. The code of the methods and metrics are provided so that they can be used as a benchmark in future comparisons. The recent YOLO8n model proves better than FootAndBall in long-shot real-time detection of the ball and players on football fields.