UMP Institutional Repository

Vehicle counting system based on vehicle type classification using deep learning method

Suryanti, Awang and Nik Mohamad Aizuddin, Nik Azmi (2017) Vehicle counting system based on vehicle type classification using deep learning method. In: 7th International Conference on IT Convergence and Security, ICITCS 2017, 25-28 September 2017 , Seoul, South Korea. pp. 1-8., 449. ISSN 18761100 ISBN: ISBN 978-981106450-0

[img] PDF
80. Vehicle Counting System based on Vehicle Type Classification using Deep Learning Method.pdf - Published Version
Restricted to Repository staff only

Download (438kB) | Request a copy
[img]
Preview
PDF
80.1 Vehicle Counting System based on Vehicle Type Classification using Deep Learning Method.pdf - Published Version

Download (56kB) | Preview

Abstract

Vehicle counting system (VCS) is one of the technologies that able to fulfil the ITS aim in providing a safe and efficient road and transportation infra-structure. This paper is aimed to provide a more accurate VCS based on vehicle type classification method rather than current implementation in existing works that only count the vehicle as vehicle and non-vehicle. To fulfil the aim, we pro-posed to use Deep Learning method with CNNLS framework to classify the ve-hicle into three classes namely car, taxi and truck. This VCS is motivated by current implementation of the traffic census in Malaysia whereby they record the vehicle based on certain classes. The biggest challenge in this paper is how to discriminate features of taxi and car since taxi has almost identical features as car. However, with our proposed method, we able to count based on correctly classified of the vehicle with the average accuracy of 90.83 %. We tested our method based on frontal view of vehicle from the self-obtained database taken using mounted-camera at the selected federal road.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Index by Scopus
Uncontrolled Keywords: Computational Intelligence; Vehicle Counting; Vehicle; Deep Learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 24 May 2018 06:20
Last Modified: 24 May 2018 06:20
URI: http://umpir.ump.edu.my/id/eprint/20567
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item