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Enhancing sports performance analysis: an ai approach for basketball and volleyball

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dc.contributor BEȘLIU, Corina
dc.contributor.author PETRANIS, Ana
dc.contributor.author VINOGRADSCHII, Mihail
dc.date.accessioned 2024-10-23T08:50:40Z
dc.date.available 2024-10-23T08:50:40Z
dc.date.issued 2024
dc.identifier.citation PETRANIS, Ana and Mihail VINOGRADSCHII. Enhancing sports performance analysis: an ai approach for basketball and volleyball. In: Conferinţa tehnico-ştiinţifică a studenţilor, masteranzilor şi doctoranzilor = Technical Scientific Conference of Undergraduate, Master and PhD Students, Universitatea Tehnică a Moldovei, 27-29 martie 2024. Chișinău, 2024, vol. 2, pp. 911-916. ISBN 978-9975-64-458-7, ISBN 978 9975-64-460-0 (Vol.2). en_US
dc.identifier.isbn 978-9975-64-458-7
dc.identifier.isbn 978 9975-64-460-0
dc.identifier.uri http://repository.utm.md/handle/5014/28262
dc.description.abstract Artificial intelligence is increasingly being used in all areas of human activity, from the familiar text editor to the cutting-edge satellite that has just entered Earth's orbit. Modern team sports also need to implement AI to analyze the results of matches, in order to identify the strengths and weaknesses of each player, as well as to develop a right strategy for subsequent games against a specific opponent. Our study investigates the process of data collection and processing for sports analytics using basketball and volleyball games as examples. Data for analysis was sourced from the official FIBA YouTube channel "FIBA - The Basketball Channel" and the Baller TV replay library, which archives matches from various youth sports. The obtained videos were segmented into individual frames. A portion of these frames were manually labeled to create training and validation datasets. The remaining frames formed the unlabeled test dataset, crucial for evaluating the accuracy of the YOLOv8 model chosen as the foundation for this study. Our focus was on identifying players through jersey number recognition, detecting the ball and its location on the court, classifying game situations, and processing the score and timer using OCR technology. The fine-tuned YOLOv8 achieved an accuracy of 93% based on the mAP50-95 metric, which evaluates the overlap between predicted and actual bounding boxes. en_US
dc.language.iso en en_US
dc.publisher Universitatea Tehnică a Moldovei en_US
dc.relation.ispartofseries Conferinţa tehnico-ştiinţifică a studenţilor, masteranzilor şi doctoranzilor = Technical Scientific Conference of Undergraduate, Master and PhD Students: Chişinău, 27-29 martie 2024. Vol. 2;
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject artificial intelligence en_US
dc.subject sports performance en_US
dc.subject machine learning en_US
dc.title Enhancing sports performance analysis: an ai approach for basketball and volleyball en_US
dc.type Article en_US


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