Soccer; Deep learning; Computer vision; artificial intelligence; challenges; Sports; Sport; Football
Résumé :
[en] SoccerNet is a large-scale video dataset for video undestanding in soccer. The dataset has been used in various research challenges, where participants competed to develop the best algorithms for tasks such as predicting events, tracking players or recognising their jersey number. The challenges have spurred advancements in the state-of-the-art in sports video analysis.
Centre/Unité de recherche :
Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science - ULiège TELIM
Disciplines :
Sciences informatiques
Auteur, co-auteur :
Cioppa, Anthony ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
Giancola, Silvio; KAUST > AI Initiative
Deliège, Adrien; Layer7
Somers, Vladimir; UCL - Catholic University of Louvain [BE] ; SportRadar ; EPLF - École Polytechnique Fédérale de Lausanne [CH]
Magera, Floriane ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
Mkhallati, Hassan; ULB - Université Libre de Bruxelles [BE] ; «X» - Ecole Polytechnique, France [FR]
Kang, Le; Baidu Research
Zhou, Xin; Baidu Research
Cheng, Zhiyu; Baidu Research
Ghanem, Bernard; KAUST > AI Initiative
Van Droogenbroeck, Marc ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Télécommunications
Langue du document :
Anglais
Titre :
SoccerNet: Exploring the Game with Computer Vision
Date de publication/diffusion :
21 février 2023
Nom de la manifestation :
Rising Stars in AI Symposium 2023
Organisateur de la manifestation :
King Abdullah University of Science and Technology (KAUST)
