Vehicle Recognition Based on Support Vector Machine

摘要

In the paper, a vehicle recognition model based on Support Vector Machine (SVM) is presented. SVM can solve the problem of nonlinear well, avoiding some difficulties including high dimensional and local minimum. This paper applies the multi-classification method based on Support Vector Machine to vehicle recognition. Support vector machine is a new theory and technology in the filed of pattern recognition and has shown excellent performance in practice. This method was proposed basing on Structural Risk Minimization (SRM) in place of Experiential Risk Minimization (ERM), thus it has good generalization capability. By mapping input data into a high dimensional characteristic space in which an optimal separating hyperplane is built, SVM presents a lot of advantages for resolving the small samples, nonlinear and high dimensional pattern recognition, as well as other machine-learning problems such as function fitting. The simulation results show the model has strong non-linear solution and anti-jamming ability, and can effectively distinguish vehicle type.