Automatic Traffic Sign Detection and Classification

Abstract

Abstract: Automatic detection and recognition of road traffic signs is an essential task for regulating the traffic, guiding, and warning the drivers and pedestrians. M a n y challenges like cluttered background, foreground scenery, various geographic, metrological, weather conditions namely cloudy day, rain, snow, fog, changeable and uncontrollable lighting conditions depending on the time of the day exhibits the detection of road signs. The traffic sign may have different sizes and colors. Our goal is to detect the traffic signs with red, blue, yellow colors with any of the available shapes. Then the detected signs, combined with the color and shape information are classified. The recognition of Traffic sign involves two stages: detection stage, it finds the region consisting of traffic signs from the image and then the classification stage where the detected signs are categorized into different classes like information, warning, prohibition and so on. The image is acquired and is processed in CSR block, in which the base features from the image are extricated. The base features extricated from the image are color, shape, and Region of Interest (ROI) position. This pre-processing is very quick, because no special transformation is required. RGB and HSV color space is chosen for color segmentation. By extracting the color, CSR block creates 3 binary maps (red, blue, and yellow). From these binary maps, the shape of the traffic sign is detected. From the image the ROI is segmented, which is the input to the shape detecting block. After the shape is detected, traffic sign is classified according to its detected color and shape.