Traffic sign classification using invariant features and Support Vector Machines

Abstract

This paper presents a novel approach to recognize traffic signs using invariant features and support vector machines (SVM). Images of traffic signs are collected by a digital camera mounted in a vehicle. They are color segmented and all objects which represent signs are extracted and normalized to 36 x 36 pixels images. Invariant features of sign rims and speed-limit sign interiors of 350 and 250 images are computed and the SVM classifier is trained with these features. Two stages of SVM are trained; the first stage determines the shape of sign rim and the second determines the pictogram of the sign. Training and testing of both SVM classifiers are done using still images. The best performance achieved is 98% for sign rims and 93% for speed limit signs.