Intelligent Road Control System Using Advanced Image Processing Techniques

Over the past few years, Support Vector Machine (SVM) has been widely used in data classification field and has already been proved as an optimal solution for both linear and nonlinear classification problems. Since the image segmentation can be considered as a type of classification, SVM can be designed as an efficient image segmentation tool. This thesis aims to develop a SVM based intelligent road transportation control system, which involved three modules: pavement inspection, vehicle tracking, and collision warning. In the pavement inspection part, the SVM is used to extract the pavement from the background in a given image. The Radon transform is then applied to the pure pavement image to classify the crack to a particular type. In the vehicle tracking part, SVM trained by Gabor and edge features is involved to segment the first frame of a given video, which captured by an in-car camera. Another Wavelet feature based SVM is utilized to tracking this specific vehicle. In the collision warning part, the Time to Collision (TTC) is calculated by the scale change method. By the comparison between the TTC and a predefined threshold value, the Forward Collision Warning (FCW) system is designed, which can inform the driver to push the brake to avoid crash. Although the traditional image processing methods can fulfill all the three tasks above, limited success has been accomplished due to the low accuracy of image segment result. The proposed SVM algorithm can be trained by the proper feature, such as RGB feature, Gabor feature, Wavelet feature, etc., which makes the system appear to be more effective and computationally more efficient.

Name thesis:degree_name

Master of Science in Electrical Engineering

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

College of Engineering

Grantor dc:publisher

University of Toledo

Year dc:date

2012

Author dc:creator

• Ouyang, Dingxin

Contributors dc:contributor

• Salari, Ezzatollah

• Electrical Engineering
• Image Processing
• Support Vector Machine
• Gabor Filter
• Wavelet Transform
• Hough Transform

Statement dc:rights

• unrestricted
• This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.

Language dc:language

English

Repository record dc:identifier

http://rave.ohiolink.edu/etdc/view?acc_num=toledo1352749656

OAI identifier oai:identifier

oai:etd.ohiolink.edu:toledo1352749656