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Automatic Vehicle Detection and Identification using Visual Features

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  • Tue, 11/07/2017 - 3:00pm - 5:00pm




Automatic Vehicle Detection and Identification using Visual Features

MSc Thesis Defense by:

Hao Lyu

Date:  Tuesday, Nov 7, 2017
Time:  3:00 pm – 5:00 pm
Location: 3105, Lambton Tower

Abstract: In recent times, a motor vehicle is the most popular transportation mechanism in the world. High accuracy and success rates are key factors in automatic vehicle detection and identification.  As the most important label on vehicles, the license plate serves as the public identification for vehicles. However, it can be stolen and affixed to different vehicles by criminals to conceal their identities. Furthermore, in some cases, the plate numbers can be the same for two cars which come from different jurisdictions. In this thesis, we propose a new vehicle identification system that provides high degree of accuracy and success rate. The proposed system consists of four stages:  license plate detection, license plate recognition, license plate province detection and vehicle shape detection. In the proposed system, the features are converted into local binary pattern (LBP) and histogram of oriented gradients (HOG) on training dataset. To reach high accuracy in real-time, a novel method is used to update the system which can automatically detect any discrepancy between the license plate and the vehicles information stored in the motor vehicle database.

Thesis Committee:
Internal Reader:  Dr. Alioune Ngom
External Reader:  Dr. Abdulkadir Hussein   
Advisor:  Dr. Imran Ahmad
Chair:  TBA  



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