Multiple Cue Based Vehicle Detection and Tracking for Road Safety

Wan, SH; Kodagoda, S; Sehestedt, SA

Multiple Cue Based Vehicle Detection and Tracking for Road Safety

Wan, SH Kodagoda, S

Sehestedt, SA

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Publisher:: University of Technology, Sydney
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 8th International Conference on Intelligent Technologies (InTech-07), 2007, pp. 340 - 345
Issue Date:: 2007-01

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Field	Value	Language
dc.contributor.author	Wan, SH	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Sehestedt, SA	en_US
dc.contributor.editor	Ha, QP	en_US
dc.contributor.editor	Kwok, NM	en_US
dc.date	2007-12-12	en_US
dc.date.issued	2007-01	en_US
dc.identifier.citation	Proceedings of the 8th International Conference on Intelligent Technologies (InTech-07), 2007, pp. 340 - 345	en_US
dc.identifier.isbn	978-1-86365-718-1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2716
dc.description.abstract	With the rise in accident related fatalities on roads, the researchers around the world are looking for solutions including integrating intelligence to vehicles. One cruicial aspects of it is the robust detection and tracking of other vehicles in the visinity. In this paper, we have proposed a probabilistic way of incorporation of several visual cues in vehicle detection and a particle filter based tracking strategy. Visual cues used are, lane markings, symmetry, entropy and shadows. Combination of visual cues provided us with robust results when compared with their individual counterparts. The definition of a region of interest lowers the computational requirements with improved robustness. Experimental results of the algorithm in Sydney urban areas are presented	en_US
dc.publisher	University of Technology, Sydney	en_US
dc.relation.ispartof	Proceedings of the 8th International Conference on Intelligent Technologies (InTech-07)	en_US
dc.relation.ispartof	International Conference on Intelligent Technologies	en_US
dc.title	Multiple Cue Based Vehicle Detection and Tracking for Road Safety	en_US
dc.type	Conference Proceeding
utslib.location	Sydney, Australia	en_US
utslib.location.activity	Sydney, Australia	en_US
utslib.for	0902 Automotive Engineering	en_US
dc.location.activity	Sydney, Australia	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
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Sydney, Australia	en_US
pubs.start-date	2007-12-12	en_US

Abstract:

With the rise in accident related fatalities on roads, the researchers around the world are looking for solutions including integrating intelligence to vehicles. One cruicial aspects of it is the robust detection and tracking of other vehicles in the visinity. In this paper, we have proposed a probabilistic way of incorporation of several visual cues in vehicle detection and a particle filter based tracking strategy. Visual cues used are, lane markings, symmetry, entropy and shadows. Combination of visual cues provided us with robust results when compared with their individual counterparts. The definition of a region of interest lowers the computational requirements with improved robustness. Experimental results of the algorithm in Sydney urban areas are presented

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http://hdl.handle.net/10453/2716