Localization and recognition of traffic signs for automated vehicle control systems

We present a computer vision system for detection and recognition of traffic signs. Such systems are required to assist drivers and for guidance and control of autonomous vehicles on roads and city streets. For experiments we use sequences of digitized photographs (to be replaced by video tapes taken from a moving car) and off-line analysis. The system contains four stages. First, region segmentation based on colour pixel classification called SRSM (Supervised Region Segmentation Method). SRSM limits the search to regions of interest in the scene (image). Second, we use edge tracing to find parts of outer edges of signs which are circular or straight, corresponding to the geometrical shapes of traffic signs (circle, triangle, rectangle, octagon). The third step is geometrical analysis of the outer edge and preliminary recognition of each candidate region, which may be a potential traffic sign. The final step in recognition uses colour combinations within each region and model matching. This system may be used for recognition of other types of objects, provided that the geometrical shape and colour content remain reasonably constant. The method is reliable, easy to implement, and fast. This differs from the road signs recognition method in the PROMETEUS (EU45), [1]. The overall structure of the approach is sketched in Fig. 1.