A method of vehicle classification using models and neural networks

Abstract

This paper presents a novel method of vehicle classification using parameterized model and neural networks. First, we propose the parameterized model, which can describe the features of vehicle. In this model, vertices and their topological structure are regarded as the key features. Then we adopt a classifier based on multi-layer perceptron networks (MLPN) to recognize vehicles. In this neural network classifier, learning algorithms based on the gradient descent method for the least exponential function error (LEFE) are adopted. Experimental results show that the parameterized model can satisfactorily and effectively describe vehicles, and the correct rate for vehicle recognition using neural networks classifier is more than 91%. This novel method can be used in real world systems such as the vehicle verifying system in toll collecting station. However, it is not difficult to adapt algorithms and improve the model to fit for other traffic scene.