Vehicle classification using neural network

Abstract

Traffic congestion is one of the major problems in the big cities. Many systems are proposed to solve this problem, for example, Intelligent Transportation System(ITS). In general, ITS consists of many subsystems including traffic monitoring. The monitoring system can provide the information of vehicles that pass through the monitoring area. This information can suggests about the status of the traffic. For this reason, developing a system that can classify vehicle with high accuracy plays important role to support ITS. The proposed system of vehicle classification using neural network presents in this paper includes other techniques i.e. vehicle detection and vehicle occlusion handling. Furthermore, Back-propagation neural network and Radial basis function network are implemented for vehicle classification. The training dataset of these two neural network classifiers is taken from a traffic video while the testing dataset is taken from another traffic video. The experimental results show that both techniques can classify vehicles only 71% accuracy because the different size of detected vehicles in those two videos. In order to solve this problem, affine transform function is implemented to scale the object of the testing video. After implementing affine transformation to the system, the accuracy of Back-propagation technique increases to 86% while Radial basis function remains the same. The experimental results illustrate that Back-propagation neural network can classify vehicles with higher accuracy and better performance than Radial basis function network.