Recognising team activities from noisy data

Bialkowski, Alina, Lucey, Patrick J., Carr, Peter, Denman, Simon, Matthews, Iain, & Sridharan, Sridha (2013) Recognising team activities from noisy data. In Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, Portland, Oregon, pp. 984-990.

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Abstract

Recently, vision-based systems have been deployed in professional sports to track the ball and players to enhance analysis of matches. Due to their unobtrusive nature, vision-based approaches are preferred to wearable sensors (e.g. GPS or RFID sensors) as it does not require players or balls to be instrumented prior to matches. Unfortunately, in continuous team sports where players need to be tracked continuously over long-periods of time (e.g. 35 minutes in field-hockey or 45 minutes in soccer), current vision-based tracking approaches are not reliable enough to provide fully automatic solutions. As such, human intervention is required to fix-up missed or false detections. However, in instances where a human can not intervene due to the sheer amount of data being generated - this data can not be used due to the missing/noisy data. In this paper, we investigate two representations based on raw player detections (and not tracking) which are immune to missed and false detections. Specifically, we show that both team occupancy maps and centroids can be used to detect team activities, while the occupancy maps can be used to retrieve specific team activities. An evaluation on over 8 hours of field hockey data captured at a recent international tournament demonstrates the validity of the proposed approach.

Impact and interest:

5 citations in Scopus
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3 citations in Web of Science®

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105 since deposited on 10 Oct 2013
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ID Code: 63232
Item Type: Conference Paper
Refereed: Yes
Keywords: Recognising Team Activities, Occupancy Maps, Sport, Noisy Data, Player detection
DOI: 10.1109/CVPRW.2013.143
ISBN: 978-0-7695-4990-3
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 [please consult the author]
Deposited On: 10 Oct 2013 01:43
Last Modified: 11 Oct 2013 06:20

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