Tracking people under heavy occlusions by layered data association

Zhang, Z; Concha, OP; Piccardi, M

Tracking people under heavy occlusions by layered data association

Zhang, Z Concha, OP Piccardi, M

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Publication Type:: Journal Article
Citation:: Multimedia Tools and Applications, 2015, 74 (17), pp. 7239 - 7259
Issue Date:: 2015-09-01

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Field	Value	Language
dc.contributor.author	Zhang, Z	en_US
dc.contributor.author	Concha, OP	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.available	2020-05-25T19:06:20Z
dc.date.issued	2015-09-01	en_US
dc.identifier.citation	Multimedia Tools and Applications, 2015, 74 (17), pp. 7239 - 7259	en_US
dc.identifier.issn	1380-7501	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35127
dc.identifier.uri	http://hdl.handle.net/10453/35125
dc.identifier.uri	http://hdl.handle.net/10453/35174
dc.identifier.uri	http://hdl.handle.net/10453/35173
dc.identifier.uri	http://hdl.handle.net/10453/35172
dc.description.abstract	© 2014, Springer Science+Business Media New York. One of the main difficulties in video tracking of people arises in scenarios where targets are repeatedly and extensively occluded by other moving objects. These types of occlusions significantly affect the measurements of the person’s position, motion, shape and appearance, posing major challenges to correct tracking and data association. In this paper, we present a method for tracking people in videos based on a simplified part-based model only loosely associated with body parts. Data association is provided by a layered data association approach which performs association at feature, part and global levels in a hierarchical fashion. Occlusions are detected and managed at the part level, with corresponding model update strategies. In addition, the tracker does not make any assumption on the target’s motion direction, thus allowing tracking to withstand abrupt sideways movements and changes of directions that frequently occur in busy scenes. Experimental results against popular trackers such as mean shift, particle filters and the recent k-shortest paths (KSP) tracker based on a variety of performance indicators and datasets including ETISEO, AVSS 2007 and PETS 2009 show the effectiveness of the proposed tracker.	en_US
dc.relation.ispartof	Multimedia Tools and Applications	en_US
dc.relation.isbasedon	10.1007/s11042-014-1979-1	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.classification	Software Engineering	en_US
dc.title	Tracking people under heavy occlusions by layered data association	en_US
dc.type	Journal Article
utslib.citation.volume	17	en_US
utslib.citation.volume	74	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.issue	17	en_US
pubs.publication-status	Published	en_US
pubs.volume	74	en_US

Abstract:

© 2014, Springer Science+Business Media New York. One of the main difficulties in video tracking of people arises in scenarios where targets are repeatedly and extensively occluded by other moving objects. These types of occlusions significantly affect the measurements of the person’s position, motion, shape and appearance, posing major challenges to correct tracking and data association. In this paper, we present a method for tracking people in videos based on a simplified part-based model only loosely associated with body parts. Data association is provided by a layered data association approach which performs association at feature, part and global levels in a hierarchical fashion. Occlusions are detected and managed at the part level, with corresponding model update strategies. In addition, the tracker does not make any assumption on the target’s motion direction, thus allowing tracking to withstand abrupt sideways movements and changes of directions that frequently occur in busy scenes. Experimental results against popular trackers such as mean shift, particle filters and the recent k-shortest paths (KSP) tracker based on a variety of performance indicators and datasets including ETISEO, AVSS 2007 and PETS 2009 show the effectiveness of the proposed tracker.

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

http://hdl.handle.net/10453/35125