Structural correlation filters combined with a Gaussian particle filter for hierarchical visual tracking

Dai, M; Xiao, G; Cheng, S; Wang, D; He, X

Structural correlation filters combined with a Gaussian particle filter for hierarchical visual tracking

Dai, M Xiao, G Cheng, S Wang, D He, X

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Neurocomputing, 2020
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Dai, M
dc.contributor.author	Xiao, G
dc.contributor.author	Cheng, S
dc.contributor.author	Wang, D
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X
dc.date.accessioned	2020-05-05T09:45:20Z
dc.date.available	2020-05-05T09:45:20Z
dc.date.issued	2020-01-01
dc.identifier.citation	Neurocomputing, 2020
dc.identifier.issn	0925-2312
dc.identifier.issn	1872-8286
dc.identifier.uri	http://hdl.handle.net/10453/140492
dc.description.abstract	© 2020 Visual tracking is a key problem for many computer vision applications such as human-computer interaction, intelligent medical diagnosis, navigation and traffic control management. Most of the existing tracking methods are mainly based on correlation filters. However, boundary effect, scale estimation and template updating have not been fully resolved. Herein, this paper presents a new hierarchical tracking method combining structural correlation filters with a Gaussian Particle Filter (GPF), named KCF-GPF. Weak KCF classifiers are constructed via a Lukas-Kanade (LK) method and the preliminary target location is presented as a weighted sum of these classifiers. Specially, a facile weight strategy is implemented to estimate the reliability of each weak classifier. On the basis of the preliminary target location, the GPF using features from a Convolutional Neural Network (CNN) is employed to predict the location and scale of a target. Extensive experiments with the OTB-2013 and the OTB-2015 databases demonstrate that the proposed algorithm performs favourably against state-of-the-art trackers.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Neurocomputing
dc.relation.isbasedon	10.1016/j.neucom.2020.02.095
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Structural correlation filters combined with a Gaussian particle filter for hierarchical visual tracking
dc.type	Journal Article
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	17 Psychology and Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
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
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2020-05-05T09:45:07Z
pubs.publication-status	Published

Abstract:

© 2020 Visual tracking is a key problem for many computer vision applications such as human-computer interaction, intelligent medical diagnosis, navigation and traffic control management. Most of the existing tracking methods are mainly based on correlation filters. However, boundary effect, scale estimation and template updating have not been fully resolved. Herein, this paper presents a new hierarchical tracking method combining structural correlation filters with a Gaussian Particle Filter (GPF), named KCF-GPF. Weak KCF classifiers are constructed via a Lukas-Kanade (LK) method and the preliminary target location is presented as a weighted sum of these classifiers. Specially, a facile weight strategy is implemented to estimate the reliability of each weak classifier. On the basis of the preliminary target location, the GPF using features from a Convolutional Neural Network (CNN) is employed to predict the location and scale of a target. Extensive experiments with the OTB-2013 and the OTB-2015 databases demonstrate that the proposed algorithm performs favourably against state-of-the-art trackers.

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http://hdl.handle.net/10453/140492