3D Multiview Basketball Players Detection and Localization Based on Probabilistic Occupancy

Yang, Y; Xu, M; Wu, W; Zhang, R; Peng, Y

3D Multiview Basketball Players Detection and Localization Based on Probabilistic Occupancy

Yang, Y Xu, M

Wu, W Zhang, R

Peng, Y

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Publication Type:: Conference Proceeding
Citation:: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018, 2019
Issue Date:: 2019-01-16

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Field	Value	Language
dc.contributor.author	Yang, Y	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.contributor.author	Wu, W	en_US
dc.contributor.author	Zhang, R https://orcid.org/0000-0002-5460-7196	en_US
dc.contributor.author	Peng, Y	en_US
dc.date.issued	2019-01-16	en_US
dc.identifier.citation	2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018, 2019	en_US
dc.identifier.isbn	9781538666029	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134359
dc.description.abstract	© 2018 IEEE. This paper addresses the issue of 3D multiview basketball players detection and localization. Existing methods for this problem typically take background subtraction as input, which limits the accuracy of localization and the performance of further object tracking. Moreover, the performance of background subtraction based methods is heavily impacted by the occlusions in crowded scenes. In this paper, we propose an innovative method which jointly implements deep learning based player detection and occupancy probability based player localization. What's more, a new Bayesian model of the localization algorithms is developed, which uses foreground information from fisheye cameras to setup meaningful initialization values in the first step of iteration, in order to not only eliminate ambiguous detection, but also accelerate computational processes. Experimental results on real basketball game data demonstrate that our methods significantly improve the performance compared with current methods, by eliminating missed and false detection, as well as increasing probabilities of positive results.	en_US
dc.relation.ispartof	2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018	en_US
dc.relation.isbasedon	10.1109/DICTA.2018.8615798	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	3D Multiview Basketball Players Detection and Localization Based on Probabilistic Occupancy	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. This paper addresses the issue of 3D multiview basketball players detection and localization. Existing methods for this problem typically take background subtraction as input, which limits the accuracy of localization and the performance of further object tracking. Moreover, the performance of background subtraction based methods is heavily impacted by the occlusions in crowded scenes. In this paper, we propose an innovative method which jointly implements deep learning based player detection and occupancy probability based player localization. What's more, a new Bayesian model of the localization algorithms is developed, which uses foreground information from fisheye cameras to setup meaningful initialization values in the first step of iteration, in order to not only eliminate ambiguous detection, but also accelerate computational processes. Experimental results on real basketball game data demonstrate that our methods significantly improve the performance compared with current methods, by eliminating missed and false detection, as well as increasing probabilities of positive results.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/134359