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Soccer broadcast video understanding has been drawing a lot of attention inrecent years within data scientists and industrial companies. This is mainlydue to the lucrative potential unlocked by effective deep learning techniquesdeveloped in the field of computer vision. In this work, we focus on the topicof camera calibration and on its current limitations for the scientificcommunity. More precisely, we tackle the absence of a large-scale calibrationdataset and of a public calibration network trained on such a dataset.Specifically, we distill a powerful commercial calibration tool in a recentneural network architecture on the large-scale SoccerNet dataset, composed ofuntrimmed broadcast videos of 500 soccer games. We further release ourdistilled network, and leverage it to provide 3 ways of representing thecalibration results along with player localization. Finally, we exploit thoserepresentations within the current best architecture for the action spottingtask of SoccerNet-v2, and achieve new state-of-the-art performances.

Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting