Vehicle classification at nighttime using eigenspaces and support vector machine

Thi, TH; Robert, K; Lu, S; Zhang, J

Vehicle classification at nighttime using eigenspaces and support vector machine

Thi, TH Robert, K Lu, S Zhang, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 1st International Congress on Image and Signal Processing, CISP 2008, 2008, 2 pp. 422 - 426
Issue Date:: 2008-09-24

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Field	Value	Language
dc.contributor.author	Thi, TH	en_US
dc.contributor.author	Robert, K	en_US
dc.contributor.author	Lu, S	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.date.issued	2008-09-24	en_US
dc.identifier.citation	Proceedings - 1st International Congress on Image and Signal Processing, CISP 2008, 2008, 2 pp. 422 - 426	en_US
dc.identifier.isbn	9780769531199	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29724
dc.description.abstract	A robust framework to classify vehicles in nighttime traffic using vehicle eigenspaces and support vector machine is presented. In this paper, a systematic approach has been proposed and implemented to classify vehicles from roadside camera video sequences. Collections of vehicle images are analyzed to obtain their representative eigenspaces. The model Support Vector Machine (SVM) built from those vehicle spaces will then become a reliable classifier for any unknown vehicle images. This approach has been implemented and proven to be robust in both speed and accuracy for vehicle classification at night. ©2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - 1st International Congress on Image and Signal Processing, CISP 2008	en_US
dc.relation.isbasedon	10.1109/CISP.2008.424	en_US
dc.title	Vehicle classification at nighttime using eigenspaces and support vector machine	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Sanya, Hainan	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
pubs.volume	2	en_US

Abstract:

A robust framework to classify vehicles in nighttime traffic using vehicle eigenspaces and support vector machine is presented. In this paper, a systematic approach has been proposed and implemented to classify vehicles from roadside camera video sequences. Collections of vehicle images are analyzed to obtain their representative eigenspaces. The model Support Vector Machine (SVM) built from those vehicle spaces will then become a reliable classifier for any unknown vehicle images. This approach has been implemented and proven to be robust in both speed and accuracy for vehicle classification at night. ©2008 IEEE.

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http://hdl.handle.net/10453/29724