Car make and model recognition under limited lighting conditions at night

2.50
Hdl Handle:
http://hdl.handle.net/10547/621889
Title:
Car make and model recognition under limited lighting conditions at night
Authors:
Boonsim, Noppakun
Abstract:
Car make and model recognition (CMMR) has become an important part of intelligent transport systems. Information provided by CMMR can be utilized when licence plate numbers cannot be identified or fake number plates are used. CMMR can also be used when automatic identification of a certain model of a vehicle by camera is required. The majority of existing CMMR methods are designed to be used only in daytime when most car features can be easily seen. Few methods have been developed to cope with limited lighting conditions at night where many vehicle features cannot be detected. This work identifies car make and model at night by using available rear view features. A binary classifier ensemble is presented, designed to identify a particular car model of interest from other models. The combination of salient geographical and shape features of taillights and licence plates from the rear view are extracted and used in the recognition process. The majority vote of individual classifiers, support vector machine, decision tree, and k-nearest neighbours is applied to verify a target model in the classification process. The experiments on 100 car makes and models captured under limited lighting conditions at night against about 400 other car models show average high classification accuracy about 93%. The classification accuracy of the presented technique, 93%, is a bit lower than the daytime technique, as reported at 98 % tested on 21 CMMs (Zhang, 2013). However, with the limitation of car appearances at night, the classification accuracy of the car appearances gained from the technique used in this study is satisfied.
Citation:
Boonsim, N. (2016) 'Car make and model recognition under limited lighting conditions at night'. PhD Thesis. University of Bedfordshire.
Publisher:
University of Bedfordshire
Issue Date:
Oct-2016
URI:
http://hdl.handle.net/10547/621889
Type:
Thesis or dissertation
Language:
en
Description:
A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of Philosophy
Appears in Collections:
PhD e-theses

Full metadata record

DC FieldValue Language
dc.contributor.authorBoonsim, Noppakunen
dc.date.accessioned2016-12-16T12:19:20Z-
dc.date.available2016-12-16T12:19:20Z-
dc.date.issued2016-10-
dc.identifier.citationBoonsim, N. (2016) 'Car make and model recognition under limited lighting conditions at night'. PhD Thesis. University of Bedfordshire.en
dc.identifier.urihttp://hdl.handle.net/10547/621889-
dc.descriptionA thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of Philosophyen
dc.description.abstractCar make and model recognition (CMMR) has become an important part of intelligent transport systems. Information provided by CMMR can be utilized when licence plate numbers cannot be identified or fake number plates are used. CMMR can also be used when automatic identification of a certain model of a vehicle by camera is required. The majority of existing CMMR methods are designed to be used only in daytime when most car features can be easily seen. Few methods have been developed to cope with limited lighting conditions at night where many vehicle features cannot be detected. This work identifies car make and model at night by using available rear view features. A binary classifier ensemble is presented, designed to identify a particular car model of interest from other models. The combination of salient geographical and shape features of taillights and licence plates from the rear view are extracted and used in the recognition process. The majority vote of individual classifiers, support vector machine, decision tree, and k-nearest neighbours is applied to verify a target model in the classification process. The experiments on 100 car makes and models captured under limited lighting conditions at night against about 400 other car models show average high classification accuracy about 93%. The classification accuracy of the presented technique, 93%, is a bit lower than the daytime technique, as reported at 98 % tested on 21 CMMs (Zhang, 2013). However, with the limitation of car appearances at night, the classification accuracy of the car appearances gained from the technique used in this study is satisfied.en
dc.language.isoenen
dc.publisherUniversity of Bedfordshireen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectrecognitionen
dc.subjectmodel recognitionen
dc.subjectlimited lightingen
dc.subjectG740 Computer Visionen
dc.subjectcar make and model recognitionen
dc.subjectCMMRen
dc.titleCar make and model recognition under limited lighting conditions at nighten
dc.typeThesis or dissertationen
dc.type.qualificationnamePhDen_GB
dc.type.qualificationlevelPhDen
dc.publisher.institutionUniversity of Bedfordshireen
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