Vehicle Recognition Based on Least Squares Support Vector Machine Model

摘要

In the paper, a vehicle recognition model based on least squares support vector machine(LSSVM) is presented. LSSVM can solve the problem of nonlinear well, avoiding some difficulties including high dimensional and local minimum. In the model, the non-sensitive loss function is replaced by quadratic loss function and the inequality constraints are replaced by equality constraints. Consequently, quadratic programming problem is simplified as the problem of solving linear equation groups, and the SVM algorithm is realized by least squares method. It is presented to choose the parameter of kernel function by dynamic way, which enhances preciseness rate of recognition. The simulation results show the model can effectively distinguish vehicle type.