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 SoccerNetv2, and achieve new state-of-the-art performances.
|Original language||English (US)|
|Title of host publication||2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)|
|State||Published - 2021|
Bibliographical noteKAUST Repository Item: Exported on 2021-09-03
Acknowledged KAUST grant number(s): OSR-CRG2017-3405
Acknowledgements: This work is supported by the DeepSport project of the Walloon Region and the FRIA, EVS Broadcast Equipment, and KAUST Office of Sponsored Research (OSR) under Award No. OSR-CRG2017-3405.