Vision Based Autonomous Vehicles Target Visual Tracking with Multiple Dynamics Models

Zhen, J; Balasuriya, A; Challa, S

Vision Based Autonomous Vehicles Target Visual Tracking with Multiple Dynamics Models

Zhen, J Balasuriya, A Challa, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of Networking, Sensing and Control 2005, 2005, pp. 1081 - 1086
Issue Date:: 2005-01

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Field	Value	Language
dc.contributor.author	Zhen, J	en_US
dc.contributor.author	Balasuriya, A	en_US
dc.contributor.author	Challa, S	en_US
dc.contributor.editor	N/A	en_US
dc.date	2005-03-19	en_US
dc.date.issued	2005-01	en_US
dc.identifier.citation	Proceedings of Networking, Sensing and Control 2005, 2005, pp. 1081 - 1086	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2347
dc.description.abstract	An approach is proposed for vision based object identification and tracking for autonomous vehicle applications. In this scheme, data from the vehicles onboard vision and motion sensors are fused to identify the target 3D dynamic features in the world coordinate. Here several simple and basic linear dynamic models are combined to make the approximation of the target's unpredicted or complex motion properties. With these basic linear dynamic models a detailed description of the 3D vision based target tracking system with the interacting multiple models (IMM) for extended Kalman filtering is presented. The target's final state estimates are obtained as a weighted combination of the outputs from each different models. Performance of the proposed interacting multiple dynamic model tracking algorithm is demonstrated through experimental results.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Proceedings of Networking, Sensing and Control 2005	en_US
dc.relation.ispartof	International Conference on Networking, Sensing and Control	en_US
dc.relation.isbasedon	10.1109/ICNSC.2005.1461348	en_US
dc.rights	© 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Vision Based Autonomous Vehicles Target Visual Tracking with Multiple Dynamics Models	en_US
dc.type	Conference Proceeding
utslib.location	USA	en_US
utslib.location.activity	Tucson, USA	en_US
utslib.for	0902 Automotive Engineering	en_US
dc.location.activity	Tucson, USA	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 Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	USA	en_US
pubs.start-date	2005-03-19	en_US

Abstract:

An approach is proposed for vision based object identification and tracking for autonomous vehicle applications. In this scheme, data from the vehicles onboard vision and motion sensors are fused to identify the target 3D dynamic features in the world coordinate. Here several simple and basic linear dynamic models are combined to make the approximation of the target's unpredicted or complex motion properties. With these basic linear dynamic models a detailed description of the 3D vision based target tracking system with the interacting multiple models (IMM) for extended Kalman filtering is presented. The target's final state estimates are obtained as a weighted combination of the outputs from each different models. Performance of the proposed interacting multiple dynamic model tracking algorithm is demonstrated through experimental results.

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http://hdl.handle.net/10453/2347