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dc.contributor.authorCioppa, Anthony
dc.contributor.authorDeliège, Adrien
dc.contributor.authorMagera, Floriane
dc.contributor.authorGiancola, Silvio
dc.contributor.authorBarnich, Olivier
dc.contributor.authorGhanem, Bernard
dc.contributor.authorDroogenbroeck, Marc Van
dc.date.accessioned2021-04-21T07:05:21Z
dc.date.available2021-04-21T07:05:21Z
dc.date.issued2021-04-19
dc.identifier.urihttp://hdl.handle.net/10754/668881
dc.description.abstractSoccer 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.
dc.description.sponsorshipThis 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.
dc.publisherarXiv
dc.relation.ispartofURL:https://soccer-net.org/
dc.relation.urlhttps://arxiv.org/pdf/2104.09333.pdf
dc.rightsArchived with thanks to arXiv
dc.titleCamera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting
dc.typePreprint
dc.contributor.departmentVisual Computing Center (VCC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.eprint.versionPre-print
dc.contributor.institutionUniversity of Liege
dc.contributor.institutionEVS Broadcast Equipment
dc.identifier.arxivid2104.09333
kaust.personGiancola, Silvio
kaust.personGhanem, Bernard
kaust.grant.numberOSR-CRG2017-3405
refterms.dateFOA2021-04-21T07:07:33Z
kaust.acknowledged.supportUnitKAUST Office of Sponsored Research (OSR)


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