CFP last date
02 December 2024
Call for Paper
January Edition
CAE solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 02 December 2024

Submit your paper
Know more
Reseach Article

Real Time Traffic Sign Detection and Recognition using Adaptive Neuro Fuzzy Inference System

by Mustain Billah, Sajjad Waheed, Kawsar Ahmed, Abu Hanifa
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 3 - Number 2
Year of Publication: 2015
Authors: Mustain Billah, Sajjad Waheed, Kawsar Ahmed, Abu Hanifa
10.5120/cae2015651877

Mustain Billah, Sajjad Waheed, Kawsar Ahmed, Abu Hanifa . Real Time Traffic Sign Detection and Recognition using Adaptive Neuro Fuzzy Inference System. Communications on Applied Electronics. 3, 2 ( October 2015), 1-5. DOI=10.5120/cae2015651877

@article{ 10.5120/cae2015651877,
author = { Mustain Billah, Sajjad Waheed, Kawsar Ahmed, Abu Hanifa },
title = { Real Time Traffic Sign Detection and Recognition using Adaptive Neuro Fuzzy Inference System },
journal = { Communications on Applied Electronics },
issue_date = { October 2015 },
volume = { 3 },
number = { 2 },
month = { October },
year = { 2015 },
issn = { 2394-4714 },
pages = { 1-5 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume3/number2/436-2015651877/ },
doi = { 10.5120/cae2015651877 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T19:43:49.267027+05:30
%A Mustain Billah
%A Sajjad Waheed
%A Kawsar Ahmed
%A Abu Hanifa
%T Real Time Traffic Sign Detection and Recognition using Adaptive Neuro Fuzzy Inference System
%J Communications on Applied Electronics
%@ 2394-4714
%V 3
%N 2
%P 1-5
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Traffic sign recognition is a major part of an automated intelligent driving vehicle or driver assistance systems.Perfect recognition of traffic sign helps an intelligent driving system giving valuable information about road signs,warnings, prohibitions thus increasing driving speed, security and decreasing risk of accident. Many techniques have been used for recognising traffic signs such as backpropagation neural network,support vector machines, convolutional neural network etc on different shaped signs. Fuzzy inference system has not been used in deep for this purpose. In this paper, we have tried to find out the capability of adaptive neuro fuzzy inference system(ANFIS) for traffic sign recognition. We have used video and image processing for detecting circular shaped signs and used ANFIS for recognizing detected signs.

References
  1. Yuji Aoyagi and Toshiyuki Asakura. A study on traffic sign recognition in scene image using genetic algorithms and neural networks. In Industrial Electronics, Control, and Instrumentation, 1996., Proceedings of the 1996 IEEE IECON 22nd International Conference on, volume 3, pages 1838–1843. IEEE, 1996.
  2. Matthew Bell. Method and system for processing captured image information in an interactive video display system, May 19 2009. US Patent 7,536,032.
  3. Boguslaw Cyganek. Circular road signs recognition with soft classifiers. Integrated Computer-Aided Engineering, 14(4):323–343, 2007.
  4. Arturo De la Escalera, J Ma Armingol, and Mario Mata. Traffic sign recognition and analysis for intelligent vehicles. Image and vision computing, 21(3):247–258, 2003.
  5. Arturo De La Escalera, Luis E Moreno, Miguel Angel Salichs, and Jos´e Mar´ia Armingol. Road traffic sign detection and classification. Industrial Electronics, IEEE Transactions on, 44(6):848–859, 1997.
  6. Chiung-Yao Fang, Chiou-Shann Fuh, PS Yen, Shen Cherng, and Sei-Wang Chen. An automatic road sign recognition system based on a computational model of human recognition processing. Computer vision and Image understanding, 96(2):237–268, 2004.
  7. Hasan Fleyeh and Mark Dougherty. Road and traffic sign detection and recognition. In Proceedings of the 16th Mini- EURO Conference and 10th Meeting of EWGT, pages 644– 653, 2005.
  8. Miguel Angel Garcia-Garrido, Miguel Angel Sotelo, and E Martm-Gorostiza. Fast traffic sign detection and recognition under changing lighting conditions. In Intelligent Transportation Systems Conference, 2006. ITSC’06. IEEE, pages 811–816. IEEE, 2006.
  9. S-H Hsu and C-L Huang. Road sign detection and recognition using matching pursuit method. Image and Vision Computing, 19(3):119–129, 2001.
  10. Rafael M Inigo, Eugene S McVey, BJ Berger, and MJWirtz. Machine vision applied to vehicle guidance. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (6):820–826, 1984.
  11. William James. Intelligent transport system, November 2 2004. US Patent 6,810,817.
  12. Jyh-Shing Roger Jang, Chuen-Tsai Sun, and Eiji Mizutani. Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [book review]. Automatic Control, IEEE Transactions on, 42(10):1482–1484, 1997.
  13. Jyh-Shing Roger Jang, Chuen-Tsai Sun, and Eiji Mizutani. Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [book review]. Automatic Control, IEEE Transactions on, 42(10):1482–1484, 1997.
  14. Auranuch Lorsakul and Jackrit Suthakorn. Traffic sign recognition for intelligent vehicle/driver assistance system using neural network on opencv. In The 4th International Conference on Ubiquitous Robots and Ambient Intelligence, 2007.
  15. Si Wei Lu. Recognition of traffic signs using a multilayer neural network. In Electrical and Computer Engineering, 1994. Conference Proceedings. 1994 Canadian Conference on, pages 833–834. IEEE, 1994.
  16. Saturnino Maldonado-Basc´on, Sergio Lafuente-Arroyo, Pedro Gil-Jimenez, Hilario G´omez-Moreno, and Francisco L´opez-Ferreras. Road-sign detection and recognition based on support vector machines. Intelligent Transportation Systems, IEEE Transactions on, 8(2):264–278, 2007.
  17. Mark S Nixon and Alberto S Aguado. Feature extraction & image processing for computer vision. Academic Press, 2012.
  18. Junyoung Park, Joonsoo Kwon, Jinwook Oh, Seungjin Lee, Joo-Young Kim, and Hoi-Jun Yoo. A 92-mw real-time traffic sign recognition system with robust illumination adaptation and support vector machine. Solid-State Circuits, IEEE Journal of, 47(11):2711–2723, 2012.
  19. Maurice Peemen, Bart Mesman, and C Corporaal. Speed sign detection and recognition by convolutional neural networks. In Proceedings of the 8th International Automotive Congress, pages 162–170, 2011.
  20. Giulia Piccioli, Enrico De Micheli, Pietro Parodi, and Marco Campani. Robust method for road sign detection and recognition. Image and Vision Computing, 14(3):209– 223, 1996.
  21. Md Zamilur Rahman and Mohammad Shameemmhossain Kawsar Ahmed. Flag identification using support vector machine. JU Journal of Information Technology (JIT), 2:11–13, 2013.
  22. S Vitabile, A Gentile, and F Sorbello. A neural network based automatic road signs recognizer. In Neural Networks, 2002. IJCNN’02. Proceedings of the 2002 International Joint Conference on, volume 3, pages 2315–2320. IEEE, 2002.
  23. Fatin Zaklouta and Bogdan Stanciulescu. Real-time traffic sign recognition in three stages. Robotics and autonomous systems, 62(1):16–24, 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Image processing ANFIS Traffic sign recognition Intelligent driving sign detection