A modern approach for positional football analysis using computer vision

In this work we aim to construct a robust pipeline for the sports analysis community in order to successfully extract useful information from broadcast football matches. We propose a fast and efficient solution, based on computer vision and machine learning methods and algorithms. Our solution provides a framework suitable not only for detecting, tracking and identify the roles of the players and staff, but also for mapping each player from their position as seen in broadcast images, to their absolute position on the field. In order to achieve this, we designed each module of the pipeline by comparing multiple solutions and choosing the most suitable ones taking into consideration the trade-off between performance and inference time. We managed to provide a system that can be used by anybody in the community by feeding a sequence of frames taken from a broadcast football match.