Traffic sign recognition using Ridge Regression and OTSU method

Traffic sign recognition using Ridge Regression and OTSU method,10.1109/IVS.2011.5940440,Yanhua Jiang,Shengyan Zhou,Yan Jiang,Jianwei Gong,Guangming X

Traffic sign recognition using Ridge Regression and OTSU method  
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This paper presents an approach to detect and recognize traffic signs present in the urban scenes in China. The algorithm is composed of three steps that are color segmentation, shape detection and pictogram recognition. In the first step Ridge Regression is used to obtain a precise segmentation in RGB color space and achieves the same good performance as many machine learning based methods while using less computation time. Recognition process include a novel feature extraction involves OTSU method, and the feature extracted is robust against illumination variations and distortions. The algorithm has been run on several thousands of images with promising results. I. INTRODUCTION RAFFIC signs are installed to guide ,warn, and regulate traffic. They supply information to help drivers operate their cars in such a way as to ensure traffic safety (1). Application of traffic sign detection and recognition system include autonomous intelligent vehicle, driving assistance and sign maintenance. Focusing on the system, the most common approaches found in the literature consist of several blocks, normally identified as segmentation, detection and recognition, an overview about the three steps is presented as follows. Color information is the most important feature that can isolate traffic signs from their surrounding environment, and color combinations have enough discriminative power to reduce the search space significantly. Most traffic sign detection and recognition algorithms start with color segmentation. Color segmentation algorithms can be divided into two categories in terms of operation space. A class are carried out in RGB or YUV space with the advantage that no transformation or just a simple one is needed, and high efficiency can be achieved (2-6). The others are implemented in HSV/HIS space, in which chromatic information can be easily separated from the lighting information (7-10). Regarding operational approach, color segmentation
Conference: Intelligent Vehicle, IEEE Symposium - IV , pp. 613-618, 2011
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