Traffic sign recognition in noisy outdoor scenes

This paper presents a noise-tolerant traffic sign recognition method by using both color and shape attributes. First a discriminant analysis is carried out to obtain the color coordinate system giving the best separation between traffic signs and other objects in the scene. Then a recognition algorithm is devised by cascading three modules: an ART2 neural network module to perform color segmentation, a log-polar-exponential grid and Fourier transformation module to extract invariant traffic sign signatures, a backpropagation neural network module to classify such signatures. The performance of this method is evaluated by examining the effect of various noise sources, which may occur in actual outdoor scenes, on the recognition rate. The results obtained indicate the noise-tolerance of the developed methodology.