GATE: A novel robust object tracking method using the particle filtering and level set method

Luo, C; Cai, X; Zhang, J

GATE: A novel robust object tracking method using the particle filtering and level set method

Luo, C Cai, X Zhang, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings - Digital Image Computing: Techniques and Applications, DICTA 2008, 2008, pp. 378 - 385
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Luo, C	en_US
dc.contributor.author	Cai, X	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Proceedings - Digital Image Computing: Techniques and Applications, DICTA 2008, 2008, pp. 378 - 385	en_US
dc.identifier.isbn	9780769534565	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29733
dc.description.abstract	This paper presents a novel algorithm for robust object tracking based on the particle filtering method employed in recursive Bayesian estimation and image segmentation and optimisation techniques employed in active contour models and level set methods. The proposed Geometric Active contour-based Tracking Estimator, namely GATE, enables particle filters to track object of interest in complex environments using merely a simple feature. GATE creates a spatial prior in the state space using shape information of the tracked object. The created spatial prior is then used to filter particles in the state space in order to reshape and refine the observation distribution of the particle filtering. This improves the performance of the likelihood model in the particle filtering, so the significantly overall improvement of the particle filtering. The promising performance of our method on real video sequences are demonstrated. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - Digital Image Computing: Techniques and Applications, DICTA 2008	en_US
dc.relation.isbasedon	10.1109/DICTA.2008.75	en_US
dc.title	GATE: A novel robust object tracking method using the particle filtering and level set method	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Canberra, ACT	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	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper presents a novel algorithm for robust object tracking based on the particle filtering method employed in recursive Bayesian estimation and image segmentation and optimisation techniques employed in active contour models and level set methods. The proposed Geometric Active contour-based Tracking Estimator, namely GATE, enables particle filters to track object of interest in complex environments using merely a simple feature. GATE creates a spatial prior in the state space using shape information of the tracked object. The created spatial prior is then used to filter particles in the state space in order to reshape and refine the observation distribution of the particle filtering. This improves the performance of the likelihood model in the particle filtering, so the significantly overall improvement of the particle filtering. The promising performance of our method on real video sequences are demonstrated. © 2008 IEEE.

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http://hdl.handle.net/10453/29733