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Open AccessArticle

Active Player Detection in Handball Scenes Based on Activity Measures

Department of Informatics University of Rijeka, Rijeka 51000, Croatia
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Sensors 2020, 20(5), 1475; https://doi.org/10.3390/s20051475
Received: 31 January 2020 / Revised: 25 February 2020 / Accepted: 5 March 2020 / Published: 8 March 2020
In team sports training scenes, it is common to have many players on the court, each with his own ball performing different actions. Our goal is to detect all players in the handball court and determine the most active player who performs the given handball technique. This is a very challenging task, for which, apart from an accurate object detector, which is able to deal with complex cluttered scenes, additional information is needed to determine the active player. We propose an active player detection method that combines the Yolo object detector, activity measures, and tracking methods to detect and track active players in time. Different ways of computing player activity were considered and three activity measures are proposed based on optical flow, spatiotemporal interest points, and convolutional neural networks. For tracking, we consider the use of the Hungarian assignment algorithm and the more complex Deep SORT tracker that uses additional visual appearance features to assist the assignment process. We have proposed the evaluation measure to evaluate the performance of the proposed active player detection method. The method is successfully tested on a custom handball video dataset that was acquired in the wild and on basketball video sequences. The results are commented on and some of the typical cases and issues are shown. View Full-Text
Keywords: object detector; object tracking; activity measure; Yolo; deep sort; Hungarian algorithm; optical flows; spatiotemporal interest points; sports scene object detector; object tracking; activity measure; Yolo; deep sort; Hungarian algorithm; optical flows; spatiotemporal interest points; sports scene
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MDPI and ACS Style

Pobar, M.; Ivasic-Kos, M. Active Player Detection in Handball Scenes Based on Activity Measures. Sensors 2020, 20, 1475. https://doi.org/10.3390/s20051475

AMA Style

Pobar M, Ivasic-Kos M. Active Player Detection in Handball Scenes Based on Activity Measures. Sensors. 2020; 20(5):1475. https://doi.org/10.3390/s20051475

Chicago/Turabian Style

Pobar, Miran; Ivasic-Kos, Marina. 2020. "Active Player Detection in Handball Scenes Based on Activity Measures" Sensors 20, no. 5: 1475. https://doi.org/10.3390/s20051475

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