Multi-camera multi-player tracking with deep player identification in sports video

Zhang, R; Wu, L; Yang, Y; Wu, W; Chen, Y; Xu, M

Multi-camera multi-player tracking with deep player identification in sports video

Zhang, R

Wu, L

Yang, Y Wu, W Chen, Y Xu, M

Permalink

Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Pattern Recognition, 2020, 102
Issue Date:: 2020-06-01

Closed Access

	Filename	Description	Size
	1-s2.0-S0031320320300650-main.pdf	Published version	3.31 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, R https://orcid.org/0000-0002-5460-7196
dc.contributor.author	Wu, L https://orcid.org/0000-0001-9346-3597
dc.contributor.author	Yang, Y
dc.contributor.author	Wu, W
dc.contributor.author	Chen, Y
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849
dc.date.accessioned	2021-03-07T20:55:31Z
dc.date.available	2021-03-07T20:55:31Z
dc.date.issued	2020-06-01
dc.identifier.citation	Pattern Recognition, 2020, 102
dc.identifier.issn	0031-3203
dc.identifier.issn	1873-5142
dc.identifier.uri	http://hdl.handle.net/10453/146871
dc.description.abstract	© 2020 Identity switches caused by inter-object interactions remain a critical problem for multi-player tracking in real-world sports video analysis. Existing approaches utilizing the appearance model is difficult to associate detections and preserve identities due to the similar appearance of players in the same team. Instead of the appearance model, we propose a distinguishable deep representation for player identity in this paper. A robust multi-player tracker incorporating with deep player identification is further developed to produce identity-coherent trajectories. The framework consists of three parts: (1) the core component, a Deep Player Identification (DeepPlayer) model that provides an adequate discriminative feature through the coarse-to-fine jersey number recognition and the pose-guided partial feature embedding; (2) an Individual Probability Occupancy Map (IPOM) model for players 3D localization with ID; and (3) a K-Shortest Path with ID (KSP-ID) model that links nodes in the flow graph by a proposed player ID correlation coefficient. With the distinguishable identity, the performance of tracking is improved. Experiment results illustrate that our framework handles the identity switches effectively, and outperforms state-of-the-art trackers on the sports video benchmarks.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Pattern Recognition
dc.relation.isbasedon	10.1016/j.patcog.2020.107260
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Multi-camera multi-player tracking with deep player identification in sports video
dc.type	Journal Article
utslib.citation.volume	102
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
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
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-07T20:55:28Z
pubs.publication-status	Published
pubs.volume	102

Abstract:

© 2020 Identity switches caused by inter-object interactions remain a critical problem for multi-player tracking in real-world sports video analysis. Existing approaches utilizing the appearance model is difficult to associate detections and preserve identities due to the similar appearance of players in the same team. Instead of the appearance model, we propose a distinguishable deep representation for player identity in this paper. A robust multi-player tracker incorporating with deep player identification is further developed to produce identity-coherent trajectories. The framework consists of three parts: (1) the core component, a Deep Player Identification (DeepPlayer) model that provides an adequate discriminative feature through the coarse-to-fine jersey number recognition and the pose-guided partial feature embedding; (2) an Individual Probability Occupancy Map (IPOM) model for players 3D localization with ID; and (3) a K-Shortest Path with ID (KSP-ID) model that links nodes in the flow graph by a proposed player ID correlation coefficient. With the distinguishable identity, the performance of tracking is improved. Experiment results illustrate that our framework handles the identity switches effectively, and outperforms state-of-the-art trackers on the sports video benchmarks.

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

http://hdl.handle.net/10453/146871