Traffic sign recognition is studied as one of the modern assistance in driving. The main purpose of it is to help driver on realizing traffic sign around the car by using computer vision enhanced to the car. In this paper, a combination method of Color-based Method and SVM is presented to do the traffic sign recognition. Color-based Method with CIELab + hue is chosen because it gives good result on localizing traffic signs. It is first used to preprocess the image to binary image. The binary image is then processed with canny to give more accurate result on detecting traffic signs. To get the traffic sign shape, the preprocessed image is checked by using Ramer-Douglas-Peucker algorithm, this algorithm will approximate each closed object shape in the preprocessed image. Detecting closed object make it unable to detect occluded and attached road signs. In order to detect occluded and attached signs, the proposed method will use two phase of detection by estimating the shape of the arc or later called the shape-arc algorithm. Approximated circle, square, or triangle images will be marked as traffic sign and processed in the recognition step with linear c-SVM. SVM classification will be based on binary images which gives 97% accuracy. The proposed method is more accurate than methods proposed by the reference papers and the detection is able to detect harder problems such as attached signs.
['David Soendoro', 'Iping Supriana']
Traffic sign recognition with Color-based Method, shape-arc estimation and SVM