IIUM Repository (IREP)

Traffic intensity monitoring using multiple object detection with traffic surveillance cameras

Hasan Gani, Muhammad Hamdan and Khalifa, Othman Omran and Gunawan, Teddy Surya and Emran, Shamsan (2017) Traffic intensity monitoring using multiple object detection with traffic surveillance cameras. In: 2017 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA 2017), 27th-29th Nov. 2017, Putrajaya.

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

Download (1MB) | Request a copy

Abstract

Object detection and tracking is a field of research that has many applications in the current generation with increasing number of cameras on the streets and lower cost for Internet of Things(IoT). In this paper, a traffic intensity monitoring system is implemented based on the Macroscopic Urban Traffic model is proposed using computer vision as its source. The input of this program is extracted from a traffic surveillance camera which has another program running a neural network classification which can identify and differentiate the vehicle type is implanted. The neural network toolbox is trained with positive and negative input to increase accuracy. The accuracy of the program is compared to other related works done and the trends of the traffic intensity from a road is also calculated. relevant articles in literature searches, great care should be taken in constructing both. Lastly the limitation and the future work is concluded.

Item Type: Conference or Workshop Item (Invited Papers)
Additional Information: 4119/60067
Uncontrolled Keywords: computer vision;traffic monitoring; Macroscopic Urban Traffic Network Model;
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Dr Teddy Surya Gunawan
Date Deposited: 14 Dec 2017 11:28
Last Modified: 10 Jul 2018 17:28
URI: http://irep.iium.edu.my/id/eprint/60067

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year