The Speed Limit Road Signs Recognition Using Hough Transformation and Multi-Class Svm

Abstract

In this paper, a method for the speed limit traffic sign recognition is proposed. The method is based on Support Vector Machines, which is one of the most efficient algorithms used for traffic sign recognition. It comprises three phases. In the preprocessing phase, RGB images are converted into HSL images in order to increase the contrast. In the detection phase, Hough Transformation is used for detecting the speed limit signs along with Gauss and Median filters for removing the noise from the detected images. The detection phase achieves accuracy of 95.3%. In the classification phase, a Histogram of Oriented Gradients descriptor for feature extraction is used together with Support Vector Machines for image classification and speed limit sign recognition. The proposed method was used on the two databases - GTSRB, German Traffic Sign Recognition Benchmark and rMASTIF, Croatian traffic sign database. The recognition accuracy of 93.75% is achieved. The presented method proves to be applicable in advancing driving assistance systems due to its detection and recognition accuracy as well as its performance, thus making it appropriate for real-time applications.