Multi-player tracking for multi-view sports videos with improved K-shortest path algorithm

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

Multi-player tracking for multi-view sports videos with improved K-shortest path algorithm

Liang, Q Wu, W Yang, Y Zhang, R

Peng, Y Xu, M

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Applied Sciences (Switzerland), 2020, 10, (3)
Issue Date:: 2020-02-01

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Field	Value	Language
dc.contributor.author	Liang, Q
dc.contributor.author	Wu, W
dc.contributor.author	Yang, Y
dc.contributor.author	Zhang, R https://orcid.org/0000-0002-5460-7196
dc.contributor.author	Peng, Y
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849
dc.date.accessioned	2021-03-11T00:59:34Z
dc.date.available	2021-03-11T00:59:34Z
dc.date.issued	2020-02-01
dc.identifier.citation	Applied Sciences (Switzerland), 2020, 10, (3)
dc.identifier.issn	1454-5101
dc.identifier.issn	2076-3417
dc.identifier.uri	http://hdl.handle.net/10453/147019
dc.description.abstract	© 2020 by the authors. Sports analysis has recently attracted increasing research efforts in computer vision. Among them, basketball video analysis is very challenging due to severe occlusions and fast motions. As a typical tracking-by-detection method, k-shortest paths (KSP) tracking framework has been well used for multiple-person tracking. While effective and fast, the neglect of the appearance model would easily lead to identity switches, especially when two or more players are intertwined with each other. This paper addresses this problem by taking the appearance features into account based on the KSP framework. Furthermore, we also introduce a similarity measurement method that can fuse multiple appearance features together. In this paper, we select jersey color and jersey number as two example features. Experiments indicate that about 70% of jersey color and 50% of jersey number over a whole sequence would ensure our proposed method preserve the player identity better than the existing KSP tracking method.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Applied Sciences (Switzerland)
dc.relation.isbasedon	10.3390/app10030864
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Multi-player tracking for multi-view sports videos with improved K-shortest path algorithm
dc.type	Journal Article
utslib.citation.volume	10
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/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-03-11T00:59:23Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	3

Abstract:

© 2020 by the authors. Sports analysis has recently attracted increasing research efforts in computer vision. Among them, basketball video analysis is very challenging due to severe occlusions and fast motions. As a typical tracking-by-detection method, k-shortest paths (KSP) tracking framework has been well used for multiple-person tracking. While effective and fast, the neglect of the appearance model would easily lead to identity switches, especially when two or more players are intertwined with each other. This paper addresses this problem by taking the appearance features into account based on the KSP framework. Furthermore, we also introduce a similarity measurement method that can fuse multiple appearance features together. In this paper, we select jersey color and jersey number as two example features. Experiments indicate that about 70% of jersey color and 50% of jersey number over a whole sequence would ensure our proposed method preserve the player identity better than the existing KSP tracking method.

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http://hdl.handle.net/10453/147019