Goal! A practical guide to soccer video understanding

The SoccerNet dataset released in 2018 marked the start of large-scale soccer analysis in academia, gathering a growing research community which now expands to the industry. Broadcast soccer video understanding is an attractive topic for graduate students with many potential applications, like highlights composition and statistics generation. Besides, it encompasses natural yet challenging tasks for computer vision professionals, such as action spotting, camera calibration, player re-identification and tracking. It also comes with specific difficulties to handle fast-paced actions, players of similar appearance and replays through various camera views. All these aspects make soccer a rich yet often overlooked playground for research.

This tutorial focuses on the practical side of building soccer video understanding pipelines: which data is available, how to annotate it, how to use it, which useful tasks can be defined, tackled, and assessed, and which challenges keep the community and industries busy. Demos with Python code will be presented step-by-step to cover a large panel of soccer-related tasks. The instructors and presenters of the tutorial are experienced scientists from academia and industry that lead the soccer research community and develop cutting-edge technologies for sports broadcasts.