  Soccer broadcast video understanding has been drawing a lot of attention in
recent years within data scientists and industrial companies. This is mainly
due to the lucrative potential unlocked by effective deep learning techniques
developed in the field of computer vision. In this work, we focus on the topic
of camera calibration and on its current limitations for the scientific
community. More precisely, we tackle the absence of a large-scale calibration
dataset and of a public calibration network trained on such a dataset.
Specifically, we distill a powerful commercial calibration tool in a recent
neural network architecture on the large-scale SoccerNet dataset, composed of
untrimmed broadcast videos of 500 soccer games. We further release our
distilled network, and leverage it to provide 3 ways of representing the
calibration results along with player localization. Finally, we exploit those
representations within the current best architecture for the action spotting
task of SoccerNet-v2, and achieve new state-of-the-art performances.
