A two-layer night-time vehicle detector

Wang, W; Shen, C; Zhang, J; Paisitkriangkrai, S

A two-layer night-time vehicle detector

Wang, W Shen, C Zhang, J

Paisitkriangkrai, S

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Publication Type:: Conference Proceeding
Citation:: DICTA 2009 - Digital Image Computing: Techniques and Applications, 2009, pp. 162 - 167
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Wang, W	en_US
dc.contributor.author	Shen, C	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	Paisitkriangkrai, S	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	DICTA 2009 - Digital Image Computing: Techniques and Applications, 2009, pp. 162 - 167	en_US
dc.identifier.isbn	9780769538662	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29730
dc.description.abstract	We present a two-layer night time vehicle detector in this work. At the first layer, vehicle headlight detection [1, 2, 3] is applied to find areas (bounding boxes) where the possible pairs of headlights locate in the image, the Haar feature based AdaBoost framework is then applied to detect the vehicle front. This approach has achieved a very promising performance for vehicle detection at night time. Our results show that the proposed algorithm can obtain a detection rate of over 90% at a very low false positive rate (1:5%). Without any code optimization, it also performs at a faster speed compared to the standard Haar feature based AdaBoost approach [4]. © 2009 IEEE.	en_US
dc.relation.ispartof	DICTA 2009 - Digital Image Computing: Techniques and Applications	en_US
dc.relation.isbasedon	10.1109/DICTA.2009.33	en_US
dc.title	A two-layer night-time vehicle detector	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Melbourne, VIC	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:

We present a two-layer night time vehicle detector in this work. At the first layer, vehicle headlight detection [1, 2, 3] is applied to find areas (bounding boxes) where the possible pairs of headlights locate in the image, the Haar feature based AdaBoost framework is then applied to detect the vehicle front. This approach has achieved a very promising performance for vehicle detection at night time. Our results show that the proposed algorithm can obtain a detection rate of over 90% at a very low false positive rate (1:5%). Without any code optimization, it also performs at a faster speed compared to the standard Haar feature based AdaBoost approach [4]. © 2009 IEEE.

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http://hdl.handle.net/10453/29730