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Automatic Vehicle Detection and Identification using Visual Features

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  • Thu, 06/08/2017 - 3:00pm - 4:30pm


Automatic Vehicle Detection and Identification using Visual Features

MSc Thesis Proposal by:

Hao Lyu

Date:  Thursday, June 8th, 2017
Time:  3: 00 pm – 4:30 pm
Location: 3105, Lambton Tower

Abstract: This thesis proposal presents an automatic vehicle detection and identification algorithm using visual features. Nowadays vehicle has become the most popular transportation method in the world. As the most important label on vehicles, license plate is typically used to identify a vehicle. However, license plate number cannot be the only identification since the plate numbers can be same for two vehicles coming from different countries or provinces. The proposed system consists of four stages to keep the accuracy high: plate detection, plate recognition, province detection and vehicle identification. For plate detection, local binary patterns are used to extract features for training and testing. Optical character recognition is used to recognize the plate number. For detection of province name, feature matching is an effective way to classify the plate. Convolutional Neural Network (CNN) will be used in vehicle type classification.

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



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