Video Analytics in Elite Soccer: A Distributed Computing Perspective
Jha, Debesh; Rauniyar, Ashish; Johansen, Håvard D.; Johansen, Dag; Riegler, Michael Alexander; Halvorsen, Pål; Bagci, Ulas
Peer reviewed, Journal article
Accepted version
Åpne
Permanent lenke
https://hdl.handle.net/11250/3023986Utgivelsesdato
2022Metadata
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Originalversjon
Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. 2022, 221-225. 10.1109/SAM53842.2022.9827827Sammendrag
Ubiquitous sensors and Internet of Things (IoT)technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training, and match analysis post-game. New methods, including machine learning, image, and video processing, have been developed for performance evaluation, allowing the analyst to track the performance of a player in real-time. Following FIFA’s 2015 approval of electronics performance and tracking system during games, performance data of a single player or the entire team is allowed to be collected using GPS-based wearables. Data from practice sessions outside the sporting arena is being collected in greater numbers than ever before. Realizing the significance of data in professional soccer, this paper presents video analytics, examines recent state-of-the-art literature in elite soccer, and summarizes existing real-time video analytics algorithms. We also discuss real-time crowdsourcing of the obtained data, tactical and technical performance, distributed computing, and its importance in video analytics and propose a future research perspective.