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

作者：A. Cioppa, A. Deliège, Floriane Magera, Silvio Giancola, Olivier Barnich, Bernard Ghanem, Marc Van Droogenbroeck · 发表于：2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · 年份：2021 · DOI：10.1109/CVPRW53098.2021.00511 · 被引用次数：80 · 研究领域：Computer Science

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.