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

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    Type
    Preprint
    Authors
    Cioppa, Anthony
    Deliège, Adrien
    Magera, Floriane
    Giancola, Silvio
    Barnich, Olivier
    Ghanem, Bernard cc
    Droogenbroeck, Marc Van
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Electrical and Computer Engineering Program
    VCC Analytics Research Group
    Visual Computing Center (VCC)
    KAUST Grant Number
    OSR-CRG2017-3405
    Date
    2021-04-19
    Permanent link to this record
    http://hdl.handle.net/10754/668881
    
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    Abstract
    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 SoccerNet-v2, and achieve new state-of-the-art performances.
    Sponsors
    This work is supported by the Deep-Sport project of the Walloon Region and the FRIA, EVSBroadcast Equipment, and KAUST Office of Sponsored Research (OSR) under Award No. OSR-CRG2017-3405.
    Publisher
    arXiv
    arXiv
    2104.09333
    Additional Links
    https://arxiv.org/pdf/2104.09333.pdf
    Collections
    Preprints; Electrical and Computer Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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