Title

Traffic Sign Detection Based On Adaboost Color Segmentation And Svm Classification

Keywords

AdaBoost; Classification; Color segmentation; Hough transform; Traffic signs

Abstract

This paper aims to present a new approach to detect traffic signs which is based on color segmentation using AdaBoost binary classifier and circular Hough Transform. The Adaboost classifier was trained to segment traffic signs images according to the desired color. A voting mechanism was invoked to establish a property curve for each of the candidates. SVM classifier was trained to classify the property curves of each object into their corresponding classes. Experiments conducted on Adaboost color segmentation under different light conditions such as sunny, cloudy, fog and snow fall have showed a performance of 95%. The proposed system was tested on two different groups of traffic signs; the warning and the prohibitory signs. In the case of warning signs, a recognition rate of 98.4% was achieved while it was 97% for prohibitory traffic signs. This test was carried out under a wide range of environmental conditions. © 2013 IEEE.

Publication Date

12-4-2013

Publication Title

IEEE EuroCon 2013

Number of Pages

2005-2010

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/EUROCON.2013.6625255

Socpus ID

84888608191 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84888608191

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