IIUM Repository (IREP)

Vehicle classification system using viola Jones and multi-layer perceptron

Saeed S, Almehmadi Tarig and Htike@Muhammad Yusof, Zaw Zaw (2016) Vehicle classification system using viola Jones and multi-layer perceptron. International Arab Journal of Information Technology, 13 (Special Issue 2016, No. 6A). pp. 965-971. ISSN 0973-4562

[img] PDF - Published Version
Restricted to Registered users only

Download (490kB) | Request a copy
[img]
Preview
PDF (WOS) - Supplemental Material
Download (131kB) | Preview

Abstract

The automatic vehicle classification system has emerged as an important field of study in image processing and machine vision technologies' implementation because of its variety of applications. Despite many alternative solutions for the classification issue, the vision-based approaches remain the dominant solutions due to their ability to provide a larger number of parameters than other approaches. To date, several approaches with various methods have been implemented to classify vehicles. The fully automatic classification systems constitute a huge barrier for unmanned applications and advanced technologies. This project presents software for a vision-based vehicle classifier using multiple Viola-Jones detectors, moment invariants features, and a multi-layer perceptron neural network to distinguish between different classes. The results obtained in this project show the system’s ability to detect and locate vehicles perfectly in real time via live camera input.

Item Type: Article (Journal)
Additional Information: 6919/55405
Uncontrolled Keywords: Automatic vehicle classification, viola-jones detection, moment invariants, neural network.
Subjects: T Technology > TL Motor vehicles. Aeronautics. Astronautics
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User: Dr. Zaw Zaw Htike
Date Deposited: 08 Feb 2017 09:46
Last Modified: 05 Oct 2017 16:14
URI: http://irep.iium.edu.my/id/eprint/55405

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year