Traffic Sign Detection based on Color Segmentation of Obscure Image Candidates: A Comprehensive Study

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Dip Nandi 1,* A. F. M. Saifuddin Saif 2 Prottoy Paul 2 Kazi Md. Zubair 2 Seemanta Ahmed Shubho 2

1. American International University-Bangladesh, Faculty of Science and Information Technology Dhaka, 1212, Bangladesh

2. American International University-Bangladesh, Faculty of Science & Information Technology Dhaka, 1212, Bangladesh

* Corresponding author.


Received: 11 Apr. 2018 / Revised: 29 Apr. 2018 / Accepted: 17 May 2018 / Published: 8 Jun. 2018

Index Terms

Color-based detection, Shape-based detection, Uncontrolled Environment, Multi-class classification


Automated Vehicular System has become a necessity in the current technological revolution. Real Traffic sign detection and recognition is a vital part of that system that will find roadside traffic signs to warn the automated system or driver beforehand of the physical conditions of roads. Mostly, researchers based on Traffic sign detection face problems such as locating the sign, classifying it and distinguishing one sign from another. The most common approach for locating and detecting traffic signs is the color information extraction method. The accuracy of color information extraction is dependent upon the selection of a proper color space and its capability to be robust enough to provide color analysis data. Techniques ranging from template matching to critical Machine Learning algorithms are used in the recognition process. The main purpose of this research is to give a review based on methods and framework of Traffic Sign Detection and Recognition solution and discuss also the current challenges of the whole solution.

Cite This Paper

Dip Nandi, A.F.M. Saifuddin Saif, Prottoy Paul, Kazi Md. Zubair, Seemanta Ahmed Shubho, " Traffic Sign Detection based on Color Segmentation of Obscure Image Candidates: A Comprehensive Study", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.6, pp. 35-46, 2018. DOI:10.5815/ijmecs.2018.06.05


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