Vehicle logo classification using bag of word descriptor and support vector machine classifier

Intelligent Transportation Systems play an important role in traffic areas such as to record vehicular traffic data. In order to improve transportation safety and security, a system with the ability to automatically extract and recognize a vehicle is needed apart from the existing plate number recog...

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Main Author:	Ng, Jia Phui
Format:	Thesis
Language:	English
Published:	2019
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/86087/1/NgJiaPhuiMSKE2019.pdf
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id	my-utm-ep.86087
record_format	uketd_dc
spelling	my-utm-ep.860872020-08-30T08:56:02Z Vehicle logo classification using bag of word descriptor and support vector machine classifier 2019 Ng, Jia Phui TK Electrical engineering. Electronics Nuclear engineering Intelligent Transportation Systems play an important role in traffic areas such as to record vehicular traffic data. In order to improve transportation safety and security, a system with the ability to automatically extract and recognize a vehicle is needed apart from the existing plate number recognition system. The detection and recognition of the vehicle type or model can be helpful in determining whether the vehicle is registered with the department of motor vehicle. Hence, this project aims at providing extra information with respect to the vehicle which is to determine the maker of the vehicles. In this project, the classification system is trained with 10 training images for each vehicle’s manufacturer. The common features for each logo will be extracted using the Speeded-Up Robust Features algorithm and then feature points will be grouped and arranged using Bag of Word representations which will then be clustered using K means clustering method. The vehicle’s classification will be determined by using Support Vector Machine classifier to classify and identify the logo of the vehicle. From the experimental results, the classification system achieved 87% and 77% for front view and side view images respectively with 1500, number of cluster. 2019 Thesis http://eprints.utm.my/id/eprint/86087/ http://eprints.utm.my/id/eprint/86087/1/NgJiaPhuiMSKE2019.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:132643 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Ng, Jia Phui Vehicle logo classification using bag of word descriptor and support vector machine classifier
description	Intelligent Transportation Systems play an important role in traffic areas such as to record vehicular traffic data. In order to improve transportation safety and security, a system with the ability to automatically extract and recognize a vehicle is needed apart from the existing plate number recognition system. The detection and recognition of the vehicle type or model can be helpful in determining whether the vehicle is registered with the department of motor vehicle. Hence, this project aims at providing extra information with respect to the vehicle which is to determine the maker of the vehicles. In this project, the classification system is trained with 10 training images for each vehicle’s manufacturer. The common features for each logo will be extracted using the Speeded-Up Robust Features algorithm and then feature points will be grouped and arranged using Bag of Word representations which will then be clustered using K means clustering method. The vehicle’s classification will be determined by using Support Vector Machine classifier to classify and identify the logo of the vehicle. From the experimental results, the classification system achieved 87% and 77% for front view and side view images respectively with 1500, number of cluster.
format	Thesis
qualification_level	Master's degree
author	Ng, Jia Phui
author_facet	Ng, Jia Phui
author_sort	Ng, Jia Phui
title	Vehicle logo classification using bag of word descriptor and support vector machine classifier
title_short	Vehicle logo classification using bag of word descriptor and support vector machine classifier
title_full	Vehicle logo classification using bag of word descriptor and support vector machine classifier
title_fullStr	Vehicle logo classification using bag of word descriptor and support vector machine classifier
title_full_unstemmed	Vehicle logo classification using bag of word descriptor and support vector machine classifier
title_sort	vehicle logo classification using bag of word descriptor and support vector machine classifier
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2019
url	http://eprints.utm.my/id/eprint/86087/1/NgJiaPhuiMSKE2019.pdf
_version_	1747818493269508096

Vehicle logo classification using bag of word descriptor and support vector machine classifier

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