Road Guidance Sign Recognition in Urban Areas by Structure

Road guidance sign localization and recognition problem in cluttered environment is considered. Detection of signs in input images is based on both color and shape properties. Road guidance signs have specific background color (green, blue or brown) and rectangular shape. First, color segmentation is applied to detect sign candidate regions. Obtained regions are grouping using 8-neighbors method. Then additional filtering by shape properties applied to discard non-rectangular regions. Typically symbols inside road guidance sign can be divided into 3 groups (except "sign-in-sign" case): arrow region, text regions with direction descriptions and region with distance to crossroad.. One of crucial moments in recognition of guidance sign is detecting arrow region and understanding of its structure. Typically this region has the biggest area among the symbols in the sign plate. Colors which are used for road signs are highly contrast. It allows extracting symbols from sign background using color information. Two different algorithms were applied to detect arrowheads: genetic algorithm and border tracing algorithm. Deformable model of arrowhead with five deformation parameters was used for genetic algorithm. Initial population was randomly distributed inside arrow region and evolutionary changed in order to achieve maximum matching. Border tracing algorithm is based in detecting corner points on the outer boundary of arrow. All corner points were checked to confirm parameters of arrowhead. The proposed algorithm localize road guidance signs in different weather and lighting conditions in day and night time with probability higher than 92%. Processing speed is high enough to apply this algorithm in time-critical application. In case of border tracing method total processing time for one image was less than 0.08 sec.