Study Of Support Vector Machine And The Applications In Intelligent Transport System

Statistical Learning Theory(SLT) is a learning theory which specializes in machine learning with finite examples. As a learning method based on SLT, Support Vector Machine(SVM) has the advantages of global solutions, good adaptivity, high generalization ability and maturity in theory. SLT and SVM are the hot-spot in the field of machine learning nowadays. With the development of communication, information and electronic technology and computer network, Intelligent Transport System(ITS) is paid more and more emphasis, it contains many parts, such as vehicle type recognition and license plate recognition.In this paper, we introduce SVM to the field of ITS, the main work is described as follows:(1) We summarize the latest research achievements and development of ITS, present the conceptions of SLT and the principles of SVM;(2) Taking the traffic sign as examples and adopting Hough transform in the stage of feature extraction, we introduce SVM to the problem of shape recognition and compare the experimental results with traditional learning methods.(3) Then we use SVM to settle the vehicle type recognition problem, where we utilize the wavelet analysis and mathematical morphology method to extract the figure feature. In the stage of test, the generalization ability of SVM exceeds other methods.(4) Lastly, SVM is used here in the problem of license plate recognition, which contains license plate location, characters segmentation and character recognition. In this paper, we make use of morphology method to locate the region of license first, and then top-hat transform is used here to segment the characters, in the stage of character recognition we adopt SVM method and the result is satisfying.