Research And Simulation Of Highway License Plate Recognition Method Based On Support Vector Machine Technology

Intelligent Transportation System (ITS) is the necessary approaches for transportation modernization, which are aimed at making full use of available road facilities, improving the reciprocity of vehicles, roads and people, enhancing system security, high-efficiency and cosines. ITS has proved its high economic and social benefits. Vehicle Recognition System is an important part of intelligent transportation system. Nevertheless the application of VRS still maintained in exploration and research stage in China. Due to the different national conditions, direct importation of foreign systems can not satisfy the requirement of complex transport system in China. Under mentioned background, the problems relate with vehicle recognition system are researched.This topic is mainly focus on the research of the vehicles recognition technology improvement. The Traditional Statistics is a gradual theory which the amount of specimen is tending to infinite,while Statistical Learning Theory(SLT)is a theory at the condition of small specimen amount.Since the specimen amount is always limited in reality, SLT has its theoretical advantage.Support Vector Machine (SVM) bases on SLT and Structure Risk Minimum (SRM) principle,thus it is not only simple in structure,but also has good generalization ability. So SVM has gradually become one of hotspot issues in the field of machine learning.We use the SVM classification algorithm in our vehicles recognition model, and have a profound research in basic methods of pattern recognition and regressive analysis,and their applications to ITS. Thus it greatly improved the correct recognition rate and accuracy in the vehicle recognition experiment.Automatic Vehicle Classification has a directly relationship of whether the intelligent transportation systems can play a huge role in the potential, thus our study on the vehicle identification technology have a significant role in realize the intelligent of modern traffic management.