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Real Time Traffic Density Count using Image Processing

by Alisha Janrao, Mudit Gupta, Divya Chandwani, U. A. Joglekar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 162 - Number 10
Year of Publication: 2017
Authors: Alisha Janrao, Mudit Gupta, Divya Chandwani, U. A. Joglekar
10.5120/ijca2017913334

Alisha Janrao, Mudit Gupta, Divya Chandwani, U. A. Joglekar . Real Time Traffic Density Count using Image Processing. International Journal of Computer Applications. 162, 10 ( Mar 2017), 8-12. DOI=10.5120/ijca2017913334

@article{ 10.5120/ijca2017913334,
author = { Alisha Janrao, Mudit Gupta, Divya Chandwani, U. A. Joglekar },
title = { Real Time Traffic Density Count using Image Processing },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 162 },
number = { 10 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 8-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume162/number10/27277-2017913334/ },
doi = { 10.5120/ijca2017913334 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:08:37.885190+05:30
%A Alisha Janrao
%A Mudit Gupta
%A Divya Chandwani
%A U. A. Joglekar
%T Real Time Traffic Density Count using Image Processing
%J International Journal of Computer Applications
%@ 0975-8887
%V 162
%N 10
%P 8-12
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays traffic jams and congestion is a common issue because of the day by day increment of numerous vehicles. A smart traffic control system can be one of the solutions to the above problem. This can be done by measuring the vehicular density on that road wherein real time image and video processing techniques will be used. The main aim is to coordinate the traffic by keeping a check of its density from all the sides and thereby controlling the traffic signal intelligently. This paper will present an algorithm so as to determine the amount of vehicles on that road. This density counting algorithm will work by the comparison between one frame of the live video (real time) and the reference image followed by looking for the vehicles in the desired region. The 0traffic signal will be controlled smartly by comparing the vehicle density and the direction of the traffic.

References
  1. Ashwini D. Bharade, Surabhi S. Gaopande, Robust and Adaptive Traffic Surveillance System for Urban Intersections on Embedded Platform, 2014 Annual IEEE India Conference (INDICON)
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  4. S.Lokesh , T.Prahlad Reddy, An Adaptive Traffic Control System Using Raspberry PI, International Journal of Engineering Sciences & Research Technology
  5. An Algorithm for Full Coverage and Real Time Traffic Density Calculation on Roads, Juma Joram Mashenene1, Xuewen Ding2, Said Kassim Katungunya3 International Journal of Science and Research (IJSR)
  6. Recognition of Car Makes and Models Froma Single Traffic-Camera Image Hongsheng He, Member, IEEE, Zhenzhou Shao, and Jindong Tan, Member, IEEE
  7. Speed Detection Camera System using Image ProcessingTechniques on Video Streams Osman Ibrahim, Hazem ElGendy, and Ahmed M. ElShafee, Member, IEEE
Index Terms

Computer Science
Information Sciences

Keywords

Traffic Density count Image Processing Intelligent Controlling of Traffic Camera Raspberry Pi Server.

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