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International Journal of Innovation and Applied Studies
ISSN: 2028-9324     CODEN: IJIABO     OCLC Number: 828807274     ZDB-ID: 2703985-7
 
 
Wednesday 22 May 2019

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A Fast and Robust Traffic Sign Recognition


Volume 5, Issue 2, February 2014, Pages 139–149

 A Fast and Robust Traffic Sign Recognition

Ali BEHLOUL1 and Yassmina SAADNA2

1 LaSTIC Laboratory at the University Hadj Lakhdar, Batna, Algeria
2 LaSTIC Laboratory at the University Hadj Lakhdar, Batna, Algeria

Original language: English

Received 20 October 2013

Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


Traffic Sign Recognition (TSR) system is an important component for the intelligent vehicles, it can assist and inform the driver about dangerous situations such as stop, icy roads, no entry or speed limits. In this paper we present a fast and robust traffic sign recognition system constituted of three modules which are: segmentation, detection and recognition of sign type. In the first module we start by applying a filter after normalization of the three RGB channels to extract red, green, blue and yellow maps. To detect the signs and identify their forms, in the second module we propose a new and fast approach for pattern recognition based on minimum bounding rectangle. For the third module, the recognition is made by using a matching directly between the SURF descriptors of the detected traffic sign and the traffic signs in the database, in this module we apply a filtering interest points detected and we keep only the points that are inside the pictogram's sign. The evaluation of the proposed approach gives good results compared to some powerful techniques. As a result, with the proposed system we have obtained a high performance with 95.65% sign detection, 97.72% traffic sign identification and 89.59% traffic sign recognition rate in an average time less than 80 ms/image.

Author Keywords: Advanced driver-assistance systems, pattern recognition, image segmentation, road traffic, interest points, image color analysis.


How to Cite this Article


Ali BEHLOUL and Yassmina SAADNA, “A Fast and Robust Traffic Sign Recognition,” International Journal of Innovation and Applied Studies, vol. 5, no. 2, pp. 139–149, February 2014.