Combating tracking drift : developing robust object tracking methods

Li, Jiatong

Combating tracking drift : developing robust object tracking methods

Li, Jiatong

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Publication Type:: Thesis
Issue Date:: 2017

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Field	Value	Language
dc.contributor.advisor	Correlation filter.	en_AU
dc.contributor.author	Li, Jiatong
dc.date.accessioned	2017-11-15T23:04:16Z
dc.date.available	2017-11-15T23:04:16Z
dc.date.issued	2017
dc.identifier.uri	http://hdl.handle.net/10453/120179
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_AU
dc.description.abstract	Visual object tracking plays an important role in many computer vision applications, such as video surveillance, unmanned aerial vehicle image processing, human computer interaction and automatic control. This research aims to develop robust object tracking methods, which are capable of tracking general object without the prior knowledge of the target. Tracker drift is one of the most challenging issues in object tracking due to target deformations, illumination variations, abrupt motions, occlusions and background clutters. This thesis focuses on the tracking drift problem, and adopts three main solutions. These include: designing an efficient target shape feature extraction method, comparing target features with metric learning and using the ensemble tracking method to tackle the tracking drift during tracker online update.	en_AU
dc.format	Thesis (PhD)
dc.language.iso	en_AU	en_AU
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/120179/7/02whole.pdf
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	au.edu.uts.lib/ppc
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Robust object tracking.	en_AU
dc.subject	Shape feature extraction techniques.	en_AU
dc.subject	Weber's law	en_AU
dc.subject.lcsh	Image processing.	en_AU
dc.title	Combating tracking drift : developing robust object tracking methods	en_AU
dc.type	Thesis	en_AU
utslib.copyright.status	open_access

Abstract:

Visual object tracking plays an important role in many computer vision applications, such as video surveillance, unmanned aerial vehicle image processing, human computer interaction and automatic control. This research aims to develop robust object tracking methods, which are capable of tracking general object without the prior knowledge of the target. Tracker drift is one of the most challenging issues in object tracking due to target deformations, illumination variations, abrupt motions, occlusions and background clutters. This thesis focuses on the tracking drift problem, and adopts three main solutions. These include: designing an efficient target shape feature extraction method, comparing target features with metric learning and using the ensemble tracking method to tackle the tracking drift during tracker online update.

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http://hdl.handle.net/10453/120179