Traffic Sign Detection and Recognition

Authors

  • Dr.Hazeem Baqer Tahar Computer Sciences Department , College of Education for pure Sciences, University of Thi-Qar.
  • Sahar Rayad Abdul kadeem Computer Sciences Department , College of Education for pure Sciences, University of Thi-Qar.

DOI:

https://doi.org/10.32792/jeps.v10i1.42

Keywords:

Traffic sign, detection and recognition, gray level, RGB, a Similarity metrics, correlation Coefficient

Abstract

Through dealing with technologies and technology, in field use image processing in the systems of
electronic Including smart driving systems for cars. In order to facilitate and improve the performance of
electronic systems. Some mathematical concepts and algorithms are employed for this purpose. Where
traffic Sign Detection and Recognition (TSDR) systems provide an additional level to driver assistance,
leading to improve safety for passengers, road users and vehicles. As part of Advanced Driving Assistance
Systems (ADAS), ADAS helps the driver to drive the car and leads to a better awareness on the road.
proposed system for the detection of traffic signs, classification into danger, information, obligation and
prohibition classes.
Through this research we aim to provide a system capable of taking images of traffic signals on both sides
of the road, Analyze and recognition its and alert the driver to what the sign image mean. The driver will
be able to act on the instructions in the traffic sign. The traffic system helps drivers recognize signs they
did not recognize before. The operation is performed using a Similarity metrics were using correlation for
Complete the matching process between the resulting image and the stored image in the database

References

Md. Safaet Hossain and Zakir Hyder Traffic "Road Sign Detection and Recognition for Automotive

Vehicles”, Department of Electrical Engineering and Computer Science North South University, Dhaka

Bangladesh,2015.

C. Fang, S. Chen, and C. Fuh, "Road-sign detection and tracking," IEEE Trans. On Vehicular

Technology, vol. 52, pp. 1329-1341, 2003.

N. Yabuki, Y. Matsuda, Y. Fukui, and S. Miki, "Region detection using color similarity," presented

at 1999 IEEE Inter. Symposium on Circuits and Systems, Orlando, Florida, USA,1999.

L. Estevez and N. Kehtarnavaz, "A real-time histographic approach to road sign recognition,"

presented at IEEE Southwest Symposium on Image Analysis and Interpretation, San Antonio, Texas,

A. de la Escalera, L. Moreno, E. Puente, and M. Salichs, "Neural traffic sign recognition for

autonomous vehicles," presented at 20th Inter. Conf. on Industrial Electronics Control and

Instrumentation, Bologna, Italy, 1994.

N. Kehtarnavaz and D. Kang, "Stop-sign recognition based on color/shape processing," Machine

Vision and Applications, vol. 6, pp. 206-208, 1993.

A. de la Escalera, J. Armingol, and M. Mata,"Traffic sign recognition and analysis for intelligent

vehicles," Image and Vision Comput., vol. 21, pp. 247-258, 2003.

H. Baqer and S. Hadi,"An Intelligent Detection Based Road Traffic Sign Recognition ", thiqar

University, college of Education for pure Sciences, Deprt of Computer Science ,2018.

S. Vitabile and F. Sorbello, "Pictogram road signs detection and understanding in outdoor

scenes," presented at Conf. Enhanced and Synthetic Vision, Orlando, Florida, 1998.

S. Vitabile, G. Pollaccia, G. Pilato, and F. Sorbello, "Road sign Recognition using a dynamic

pixel aggregation technique in the HSV color space," presented at 11th Inter. Conf. Image Analysis

and Processing, Palermo, Italy, 2001.

S. Vitabile, A. Gentile, and F. Sorbello, "A neural network based automatic road sign

recognizer," presented at The 2002 Inter. Joint Conf. on Neural Networks, Honolulu, HI, USA, 2002.

G. Jiang and T. Choi, "Robust detection of landmarks in color image based on fuzzy set theory,"

presented at Fourth Inter. Conf. on Signal Processing, Beijing, China, 1998.

N. Hoose, "Computer Image Processing in Traffic Engineering". New York: John Wiley &

sonsInc., 1991.

P. Parodi and G. Piccioli, "A feature-based recognition scheme for traffic scenes," presented at

Intelligent Vehicles '95 Symposium, Detroit, USA, 1995.

M. Lalonde and Y. Li, "Road sign recognition. Technical report, Center de recherché

informatique de Montrèal, Survey of the state of Art for sub-Project 2.4, CRIM/IIT," 1995.

J. Miura, T. Kanda, and Y. Shirai, "An active vision system for real-time traffic sign recognition,"

presented at 2000 IEEE Intelligent Transportation Systems, Dearborn, MI, USA, 2000.

S. Buluswar and B. Draper, "Color recognition in outdoor images," presented at Inter. Conf.

Computer vision, Bombay, India, 1998.

V. van," Computational Intelligence in Traffic Sign Recognition", Vrije Universiteit Faculty of

Exact Sciences Business Mathematics and Informatics De Boelelaan ,2009.

C. Rafael and E. woods, “Digital image processing”, prentice hall,2002

H. Majeed,"A New Algorithm for Shape Detection", Department of Computer Science / College

of Computer Science and IT / Nawroz University / Kurdistan Region of Iraq,2017.

P. Soille, “Morphological Image Analysis”, Springer-Verlag Berlin Heidelberg ,2004.

T. Hak and J. Dul,"Pattern matching", ERASMUS RESEARCH INSTITUTE OF

MANAGEMENT, Rotterdam School of Management, Version November 2008.

A. Asuero, A. Sayago, and A. Gonzalez,"The Correlation Coefficient: An Overview, "

Department of Analytical Chemistry, Faculty of Pharmacy, The University of Seville, Seville, Spain, 2006

Downloads

Published

2020-12-03

Issue

Section

Articles