Vehicle Recognition Based on Support Vector Machine
Bo Di Cui, Tongze Xue, Kuihe Yang · 2008
In some developed countries, the automatic vehicle recognition is a quite mature technology. This paper applies the multi-classification method based on support vector machine (SVM) to vehicle recognition. Support vector machine, appeared recently, 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 that the proposed method is effective and feasible.