Multiobject Tracking by Submodular Optimization

Shen, J; Liang, Z; Liu, J; Sun, H; Shao, L; Tao, D

Multiobject Tracking by Submodular Optimization

Shen, J Liang, Z Liu, J Sun, H Shao, L Tao, D

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Cybernetics, 2019, 49 (6), pp. 1990 - 2001
Issue Date:: 2019-06-01

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Field	Value	Language
dc.contributor.author	Shen, J	en_US
dc.contributor.author	Liang, Z	en_US
dc.contributor.author	Liu, J	en_US
dc.contributor.author	Sun, H	en_US
dc.contributor.author	Shao, L	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2019-06-01	en_US
dc.identifier.citation	IEEE Transactions on Cybernetics, 2019, 49 (6), pp. 1990 - 2001	en_US
dc.identifier.issn	2168-2267	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132248
dc.description.abstract	© 2013 IEEE. In this paper, we propose a new multiobject visual tracking algorithm by submodular optimization. The proposed algorithm is composed of two main stages. At the first stage, a new selecting strategy of tracklets is proposed to cope with occlusion problem. We generate low-level tracklets using overlap criteria and min-cost flow, respectively, and then integrate them into a candidate tracklets set. In the second stage, we formulate the multiobject tracking problem as the submodular maximization problem subject to related constraints. The submodular function selects the correct tracklets from the candidate set of tracklets to form the object trajectory. Then, we design a connecting process which connects the corresponding trajectories to overcome the occlusion problem. Experimental results demonstrate the effectiveness of our tracking algorithm. 11 Our source code is available at https://github.com/shenjianbing/submodulartrack.	en_US
dc.relation	http://purl.org/au-research/grants/arc/LP150100671
dc.relation	http://purl.org/au-research/grants/arc/DP140102164
dc.relation.ispartof	IEEE Transactions on Cybernetics	en_US
dc.relation.isbasedon	10.1109/TCYB.2018.2803217	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Multiobject Tracking by Submodular Optimization	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	49	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	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	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	49	en_US

Abstract:

© 2013 IEEE. In this paper, we propose a new multiobject visual tracking algorithm by submodular optimization. The proposed algorithm is composed of two main stages. At the first stage, a new selecting strategy of tracklets is proposed to cope with occlusion problem. We generate low-level tracklets using overlap criteria and min-cost flow, respectively, and then integrate them into a candidate tracklets set. In the second stage, we formulate the multiobject tracking problem as the submodular maximization problem subject to related constraints. The submodular function selects the correct tracklets from the candidate set of tracklets to form the object trajectory. Then, we design a connecting process which connects the corresponding trajectories to overcome the occlusion problem. Experimental results demonstrate the effectiveness of our tracking algorithm. 11 Our source code is available at https://github.com/shenjianbing/submodulartrack.

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http://hdl.handle.net/10453/132248