Real time road signs classification

This paper describes a method for classifying road signs based on a single color camera mounted on a moving vehicle. The main focus will be on the final neural network based classification stage of the candidates provided by an existing traffic sign detection algorithm. Great attention is paid to image preprocessing in order to provide a more simple and clear input to the network: candidate color images are cropped and converted to greyscale, then enhanced using a contrast stretching technique; a multi-layer perceptron neural network is then used to provide a matching score with different road sign models. Finally results are filtered using tracking. Benchmarks are presented, showing that the system is able to classify more then 200 different Italian road sign in real-time, with a recognition rate of 80% to 90%.