Interacting Tracklets for Multi-Object Tracking

Lan, L; Wang, X; Zhang, S; Tao, D; Gao, W; Huang, TS

Interacting Tracklets for Multi-Object Tracking

Lan, L Wang, X Zhang, S Tao, D

Gao, W Huang, TS

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2018, 27 (9), pp. 4585 - 4597
Issue Date:: 2018-09-01

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Field	Value	Language
dc.contributor.author	Lan, L	en_US
dc.contributor.author	Wang, X	en_US
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Gao, W	en_US
dc.contributor.author	Huang, TS	en_US
dc.date.issued	2018-09-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2018, 27 (9), pp. 4585 - 4597	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132068
dc.description.abstract	© 1992-2012 IEEE. In this paper, we propose to exploit the interactions between non-associable tracklets to facilitate multi-object tracking. We introduce two types of tracklet interactions, close interaction and distant interaction. The close interaction imposes physical constraints between two temporally overlapping tracklets, and more importantly, allows us to learn local classifiers to distinguish targets that are close to each other in the spatiotemporal domain. The distant interaction, on the other hand, accounts for the higher order motion and appearance consistency between two temporally isolated tracklets. Our approach is modeled as a binary labeling problem and solved using the efficient quadratic pseudo-Boolean optimization. It yields promising tracking performance on the challenging PETS09 and MOT16 dataset.	en_US
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2018.2843129	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Interacting Tracklets for Multi-Object Tracking	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	27	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	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
utslib.copyright.status	closed_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	27	en_US

Abstract:

© 1992-2012 IEEE. In this paper, we propose to exploit the interactions between non-associable tracklets to facilitate multi-object tracking. We introduce two types of tracklet interactions, close interaction and distant interaction. The close interaction imposes physical constraints between two temporally overlapping tracklets, and more importantly, allows us to learn local classifiers to distinguish targets that are close to each other in the spatiotemporal domain. The distant interaction, on the other hand, accounts for the higher order motion and appearance consistency between two temporally isolated tracklets. Our approach is modeled as a binary labeling problem and solved using the efficient quadratic pseudo-Boolean optimization. It yields promising tracking performance on the challenging PETS09 and MOT16 dataset.

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http://hdl.handle.net/10453/132068