Prototype-based budget maintenance for tracking in depth videos

Awwad, S; Piccardi, M

Prototype-based budget maintenance for tracking in depth videos

Awwad, S Piccardi, M

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Publication Type:: Journal Article
Citation:: Multimedia Tools and Applications, 2017, 76 (20), pp. 21117 - 21132
Issue Date:: 2017-10-01

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Field	Value	Language
dc.contributor.author	Awwad, S	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.available	2020-05-25T19:11:53Z
dc.date.issued	2017-10-01	en_US
dc.identifier.citation	Multimedia Tools and Applications, 2017, 76 (20), pp. 21117 - 21132	en_US
dc.identifier.issn	1380-7501	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117674
dc.description.abstract	© 2016, Springer Science+Business Media New York. The use of conventional video tracking based on color or gray-level videos often raises concerns about the privacy of the tracked targets. To alleviate this issue, this paper presents a novel tracker that operates solely from depth data. The proposed tracker is designed as an extension of the popular Struck algorithm which leverages the effective framework of structural SVM. The main contributions of our paper are: i) a dedicated depth feature based on local depth patterns, ii) a heuristic for handling view occlusions in depth frames, and iii) a technique for keeping the number of the support vectors within a given “budget” so as to limit computational costs. Experimental results over the challenging Princeton Tracking Benchmark (PTB) dataset report a remarkable accuracy compared to the original Struck tracker and other state-of-the-art trackers using depth and RGB data.	en_US
dc.relation.ispartof	Multimedia Tools and Applications	en_US
dc.relation.isbasedon	10.1007/s11042-016-4053-3	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.classification	Software Engineering	en_US
dc.title	Prototype-based budget maintenance for tracking in depth videos	en_US
dc.type	Journal Article
utslib.citation.volume	20	en_US
utslib.citation.volume	76	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computing and Communications
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/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.issue	20	en_US
pubs.publication-status	Published	en_US
pubs.volume	76	en_US

Abstract:

© 2016, Springer Science+Business Media New York. The use of conventional video tracking based on color or gray-level videos often raises concerns about the privacy of the tracked targets. To alleviate this issue, this paper presents a novel tracker that operates solely from depth data. The proposed tracker is designed as an extension of the popular Struck algorithm which leverages the effective framework of structural SVM. The main contributions of our paper are: i) a dedicated depth feature based on local depth patterns, ii) a heuristic for handling view occlusions in depth frames, and iii) a technique for keeping the number of the support vectors within a given “budget” so as to limit computational costs. Experimental results over the challenging Princeton Tracking Benchmark (PTB) dataset report a remarkable accuracy compared to the original Struck tracker and other state-of-the-art trackers using depth and RGB data.

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

http://hdl.handle.net/10453/117674