Scaling up SoccerNet with multi-view spatial localization and re-identification
KAUST DepartmentKing Abdullah University of Science and Technology, Image and Video Understanding Laboratory, 23955, Thuwal, Saudi Arabia
Visual Computing Center (VCC)
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Electrical and Computer Engineering Program
Permanent link to this recordhttp://hdl.handle.net/10754/679244
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AbstractSoccer videos are a rich playground for computer vision, involving many elements, such as players, lines, and specific objects. Hence, to capture the richness of this sport and allow for fine automated analyses, we release SoccerNet-v3, a major extension of the SoccerNet dataset, providing a wide variety of spatial annotations and cross-view correspondences. SoccerNet’s broadcast videos contain replays of important actions, allowing us to retrieve a same action from different viewpoints. We annotate those live and replay action frames showing same moments with exhaustive local information. Specifically, we label lines, goal parts, players, referees, teams, salient objects, jersey numbers, and we establish player correspondences between the views. This yields 1,324,732 annotations on 33,986 soccer images, making SoccerNet-v3 the largest dataset for multi-view soccer analysis. Derived tasks may benefit from these annotations, like camera calibration, player localization, team discrimination and multi-view re-identification, which can further sustain practical applications in augmented reality and soccer analytics. Finally, we provide Python codes to easily download our data and access our annotations.
CitationCioppa, A., Deliège, A., Giancola, S., Ghanem, B., & Van Droogenbroeck, M. (2022). Scaling up SoccerNet with multi-view spatial localization and re-identification. Scientific Data, 9(1). https://doi.org/10.1038/s41597-022-01469-1
SponsorsThis work was supported by the Service Public de Wallonie (SPW) Recherche under the DeepSport project and Grant N°. 2010235 (ARIAC by https://DigitalWallonia4.ai), the FRIA, and KAUST Office of Sponsored Research through the Visual Computing Center (VCC) funding.
PublisherSpringer Science and Business Media LLC
Except where otherwise noted, this item's license is described as Archived with thanks to Scientific Data under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0