Traffic Sign Detection System for Locating Road Intersections and Roundabouts: The Chilean Case

This paper presents a traffic sign detection method for signs close to road intersections and roundabouts, such as stop and yield (give way) signs. The proposed method relies on statistical templates built using color information for both segmentation and classification. The segmentation method uses the RGB-normalized (ErEgEb) color space for ROIs (Regions of Interest) generation based on a chromaticity filter, where templates at 10 scales are applied to the entire image. Templates consider the mean and standard deviation of normalized color of the traffic signs to build thresholding intervals where the expected color should lie for a given sign. The classification stage employs the information of the statistical templates over YCbCr and ErEgEb color spaces, for which the background has been previously removed by using a probability function that models the probability that the pixel corresponds to a sign given its chromaticity values. This work includes an analysis of the detection rate as a function of the distance between the vehicle and the sign. Such information is useful to validate the robustness of the approach and is often not included in the existing literature. The detection rates, as a function of distance, are compared to those of the well-known Viola-Jones method. The results show that for distances less than 48 m, the proposed method achieves a detection rate of 87.5 % and 95.4 % for yield and stop signs, respectively. For distances less than 30 m, the detection rate is 100 % for both signs. The Viola-Jones approach has detection rates below 20 % for distances between 30 and 48 m, and barely improves in the 20-30 m range with detection rates of up to 60 % . Thus, the proposed method provides a robust alternative for intersection detection that relies on statistical color-based templates instead of shape information. The experiments employed videos of traffic signs taken in several streets of Santiago, Chile, using a research platform implemented at the Robotics and Automation Laboratory of PUC to develop driver assistance systems.