Traffic sign detection based on AdaBoost color segmentation and SVM classification

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.