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    SoccerNet-v2 : A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos

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    Type
    Preprint
    Authors
    Deliège, Adrien
    Cioppa, Anthony
    Giancola, Silvio
    Seikavandi, Meisam J.
    Dueholm, Jacob V.
    Nasrollahi, Kamal
    Ghanem, Bernard cc
    Moeslund, Thomas B.
    Droogenbroeck, Marc Van
    KAUST Department
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    King Abdullah University of Science and Technology.
    Electrical Engineering Program
    Date
    2020-11-26
    Permanent link to this record
    http://hdl.handle.net/10754/666179
    
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    Abstract
    Understanding broadcast videos is a challenging task in computer vision, as it requires generic reasoning capabilities to appreciate the content offered by the video editing. In this work, we propose SoccerNet-v2, a novel large-scale corpus of manual annotations for the SoccerNet video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production. Specifically, we release around 300k annotations within SoccerNet's 500 untrimmed broadcast soccer videos. We extend current tasks in the realm of soccer to include action spotting, camera shot segmentation with boundary detection, and we define a novel replay grounding task. For each task, we provide and discuss benchmark results, reproducible with our open-source adapted implementations of the most relevant works in the field. SoccerNet-v2 is presented to the broader research community to help push computer vision closer to automatic solutions for more general video understanding and production purposes.
    Publisher
    arXiv
    arXiv
    2011.13367
    Additional Links
    https://arxiv.org/pdf/2011.13367
    Collections
    Preprints; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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