Vehicle detection and classification system based on virtual detection zone

Abstract

This paper proposes a vehicle detection and classification system based on virtual detection zone (VDZ). The proposed system consists of four main steps: foreground extraction, vehicle detection, vehicle feature extraction and vehicle classification. A moving vehicle is firstly detected based on Gaussian mixture model (GMM). Then, several techniques including region of interest selection, adaptive morphological operation, and contour processing are applied to obtain correct foreground objects. Next, vehicle features are calculated when the centroid of a vehicle is on the VDZ. Finally, vehicles are classified by using k-nearest neighbor classifier. Experimental results show that the proposed method can accurately detect and classify vehicles with an accuracy of 98.53%.