Research And Implementation Of Traffic Sign Recognition Technology Based On Color Feature And SVM

With the fast development of society and economy, the jamming and huddling of traffic become more and more serious, and become one of the bottle-neck of our modern city, traffic accidents are paid much attention by the governments of the world. In this kind of conditions, people began to study in Intelligent Traffic System (ITS). It involves the technologies of Pattern recognition, digital image Processing, artificial intelligence, electronic technology, communication technology system engineering and so on.The traffic sign recognition(TSR) the important component of intelligent vehicle, it collecting and recognition the road sign information in the vehicle driving process, then gave alert or warnings to driver, or control the operation of the vehicle directly, to keep the transportation smoothing and avoiding traffic accident:The automatic segmentation of road sign and recognition is the important support software of the intelligent transportation system, there are important theory and practical values.The keystone and difficulty lies in the segment of traffic sign, feature abstracting and the design of classifier. Traffic signs recognition consisted of two main stages:the first was traffic signs detection and the second was traffic signs recognition. We needed to do the image pre-processing, image segmentation based on color space, image binarization processing and traffic signs location.The second stage was the traffic sign recognition, and the two grades classification and recognition SVM-based was adopted. Edge orientation histogram was adopted for describing the external contour of traffic sign in the first classification. In this paper an improved grid search method was proposed. Firstly, we searched a set of parameters in a large space and then searched accurately around the parameters we had found. Finally, the traffic signs were divided into three categories:round, triangle and rectangle.Hu invariant moments were adopted for describing the regional feature of traffic signs in the secondary classification and made a further recognition of the traffic signs. Finally, standard images and text commentaries were showed. The result indicates that the algorithm has better accuracy rate and the robustness is also better under different situations.