Sensor Fusion based 3D Target Visual Tracking for Autonomous Vehicles with IMM

Zhen, J; Balasuriya, A; Challa, S

Sensor Fusion based 3D Target Visual Tracking for Autonomous Vehicles with IMM

Zhen, J Balasuriya, A Challa, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of 2005 IEEE International Conference on Robotics and Automation, 2005, pp. 1829 - 1834
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	2005-04-18	en_US
dc.date.issued	2005-01	en_US
dc.identifier.citation	Proceedings of 2005 IEEE International Conference on Robotics and Automation, 2005, pp. 1829 - 1834	en_US
dc.identifier.isbn	0-7803-8914-X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2845
dc.description.abstract	This paper proposes an approach for object identification and tracking for autonomous vehicle application. In this scheme, data from the vehicle s 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 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 model. 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 2005 IEEE International Conference on Robotics and Automation	en_US
dc.relation.ispartof	IEEE International Conference on Robots and Automation	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	Sensor Fusion based 3D Target Visual Tracking for Autonomous Vehicles with IMM	en_US
dc.type	Conference Proceeding
utslib.location	Piscataway, USA	en_US
utslib.location.activity	Barcelona, Spain	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0910 Manufacturing Engineering	en_US
dc.location.activity	Barcelona, Spain	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	Piscataway, USA	en_US
pubs.start-date	2005-04-18	en_US

Abstract:

This paper proposes an approach for object identification and tracking for autonomous vehicle application. In this scheme, data from the vehicle s 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 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 model. Performance of the proposed interacting multiple dynamic model tracking algorithm is demonstrated through experimental results.

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http://hdl.handle.net/10453/2845