CFP last date
20 December 2024
Reseach Article

Road Traffic Monitoring by intelligence-driven window based image analysis

by Shivam Tyagi, Vikas Tripathi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 6
Year of Publication: 2012
Authors: Shivam Tyagi, Vikas Tripathi
10.5120/6266-8419

Shivam Tyagi, Vikas Tripathi . Road Traffic Monitoring by intelligence-driven window based image analysis. International Journal of Computer Applications. 44, 6 ( April 2012), 15-20. DOI=10.5120/6266-8419

@article{ 10.5120/6266-8419,
author = { Shivam Tyagi, Vikas Tripathi },
title = { Road Traffic Monitoring by intelligence-driven window based image analysis },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 6 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 15-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number6/6266-8419/ },
doi = { 10.5120/6266-8419 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:34:50.156256+05:30
%A Shivam Tyagi
%A Vikas Tripathi
%T Road Traffic Monitoring by intelligence-driven window based image analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 6
%P 15-20
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The term Traffic Analysis is the process to identify the different aspects of traffic and resolve the problems in concerning areas. These traffic problems may belong to traffic congestion and in order to remove this traffic congestion, determination of Road Traffic Volume or Road Traffic Identification is expected. There are several techniques [6], [9], [11] of determining traffic volume on a specific road or highway such as Road Traffic Volume Detection by using LASER Sensors [11], Road Traffic Volume Detection by using inbuilt electromagnetic loops installed in roads and Road Traffic Volume Detection by using concepts of Digital Image Processing [3], [4], [5]. Now these techniques have their pro and cons. But by far the best technique, from the point of view of Expense and Results, is Digital Image Processing. The goal of this paper is to analyze the density of road traffic by using the techniques and methods of Digital Image Processing and we are achieving this goal by using an intelligent window based system which has the capability to enhance its power according to the need of the system.

References
  1. Boris S Kerner, Sergey L Klenov and Dietrich E Wolf. Journal of Physics A: Mathematical and General, Volume 35, Number 47, 2002. Cellular automata approach to three-phase traffic theory.
  2. Martin Treibe, Arne Kestinga, Dirk Helbingb. Transportation Research Part B: Methodological Volume 44, Issues 8–9, September–November 2010. Three-phase traffic theory and two-phase models with a fundamental diagram in the light of empirical stylized facts.
  3. Antonio Fernández-Caballero, Francisco J. Gómez, Juan López-López. Expert Systems with Applications, Volume 35, Issue 3, October 2008. Road-traffic monitoring by knowledge-driven static and dynamic image analysis.
  4. Baik Hoh, Marco Gruteser, Ryan Herring, Jeff Ban, Daniel Work , Juan-Carlos Herrera, Alexandre M. Bayen, Murali Annavaram, Quinn Jacobson. MobiSys 2008, 6th international conference on Mobile systems, applications, and services. Virtual trip lines for distributed privacy-preserving traffic monitoring.
  5. Nicolas Hautiere, Erwan Bigorgne, Jérémie Bossu, Didier Aubert. The Eighth International Workshop on Visual Surveillance - VS2008. Meteorological Conditions Processing for Vision-based Traffic Monitoring.
  6. Vargas, Toral, Barrero, Milla. Intelligent Transportation Systems(ITSC), Oct. 2008. An Enhanced Background Estimation Algorithm for Vehicle Detection in Urban Traffic Video.
  7. Jutaek Oh, Joonyoung Min, Myungseob Kim, Hanseon Cho. Transportation Research Record: Journal of the Transportation Research Board, January 2010. Development of an Automatic Traffic Conflict Detection System Based on Image Tracking Technology
  8. Robert Bain. Transportation, Volume 36, Number 5, February 2009. Error and optimism bias in toll road traffic forecasts.
  9. Prashanth Mohan, Venkata N. Padmanabhan, Ramachandran Ramjee. SenSys 2008, 6th ACM conference on Embedded network sensor systems. Rich monitoring of road and traffic conditions using mobile smartphones.
  10. Yegor Malinovskiy, Yao-Jan Wu, Yinhai Wang. Transportation Research Record: Journal of the Transportation Research Board, December 2009. Video-Based Vehicle Detection and Tracking Using Spatiotemporal Maps.
  11. Shehata, Jun Cai, Badawy,Burr, Pervez, Johannesson,Radmanesh. Intelligent Transportation Systems, June 2008. Video-Based Automatic Incident Detection for Smart Roads: The Outdoor Environmental Challenges Regarding False Alarms.
Index Terms

Computer Science
Information Sciences

Keywords

Intelligence-driven Window Frames Generation Identification Of Vehicles Video Regeneration Problem Description Parallel Vehicle