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dc.contributor.authorJiang, K
dc.contributor.authorIzadi, M
dc.contributor.authorLiu, Z
dc.contributor.authorDong, JS
dc.date.accessioned2021-04-29T03:41:45Z
dc.date.available2021-04-29T03:41:45Z
dc.date.issued2020
dc.identifier.isbn9781728185583
dc.identifier.doi10.1109/ICECCS51672.2020.00014
dc.identifier.urihttp://hdl.handle.net/10072/404021
dc.description.abstractWe are in the era that sport is increasingly defined by data. Rich data is a powerful enabling foundation for novel insights on the game and on player actions, and consequently used for fan engagement and better decision making. Detailed data that has specifications of each shot is currently missing in tennis, as the game dynamics is fast and it is beyond human ability to manually record all specifications of each and every shot, even using scoring software. In this paper, we present an intelligent system to automatically recognize player actions, ball and player movements, and important game events. The system annotates the video with a suitable set of keywords for fast retrieval in broadcast production. Various techniques of computer vision, alignment, filtering, and pre-trained deep learning models are utilized by our system. The evaluation of our results on multiple broadcast videos show great accuracy and timeliness. The implications of the work presented in this paper are profound in the current workflow in broadcast coverage of a tennis match where normally multiple video operators and judges are needed to identify events and retrieve the related clips from multiple cameras.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherIEEE
dc.relation.ispartofconferencenameICECCS - 2020 25th International Conference on Engineering of Complex Computer Systems
dc.relation.ispartofconferencetitleProceedings of the IEEE International Conference on Engineering of Complex Computer Systems, ICECCS
dc.relation.ispartofdatefrom2020-10-28
dc.relation.ispartofdateto2020-10-31
dc.relation.ispartoflocationSinagpore
dc.relation.ispartofpagefrom53
dc.relation.ispartofpageto62
dc.subject.fieldofresearchInformation systems
dc.subject.fieldofresearchcode4609
dc.titleDeep Learning Application in Broadcast Tennis Video Annotation
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationJiang, K; Izadi, M; Liu, Z; Dong, JS, Deep Learning Application in Broadcast Tennis Video Annotation, Proceedings of the IEEE International Conference on Engineering of Complex Computer Systems, ICECCS, 2020, 2020-October, pp. 53-62
dc.date.updated2021-04-28T23:52:58Z
gro.hasfulltextNo Full Text
gro.griffith.authorDong, Jin-Song


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    Contains papers delivered by Griffith authors at national and international conferences.

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