Fast traffic sign detection and recognition under changing lighting conditions

In this work a system for traffic-sign detection and classification is shown. It is intended for both prohibition and obligation circular signs and for advertising triangular ones. The system is divided into three stages: first, detection, using the Hough transform from the information of the edges of the image; second, classification, using a neural network, and third, tracking, making use of a Kalman filter, which provides the system with memory. Some results are presented, obtained by real images recorded by only one camera placed on board a conventional vehicle, in sunny days, and also cloudy, rainy ones or at night, in order to show the reliability and robustness of the system. The average processing time is 30 ms per frame, what makes the system a good approach to work in real time conditions