Road vehicle classification using Support Vector Machines

Abstract

The support vector machine (SVM) provides a robust, accurate and effective technique for pattern recognition and classification. Although the SVM is essentially a binary classifier, it can be adopted to handle multi-class classification tasks. The conventional way to extent the SVM to multi-class scenarios is to decompose an m-class problem into a series of two-class problems, for which either the one-vs-one (OVO) or one-vs-all (OVA) approaches are used. In this paper, a practical and systematic approach using a kernelised SVM is proposed and developed such that it can be implemented in embedded hardware within a road-side camera. The foreground segmentation of the vehicle is obtained using a Gaussian mixture model background subtraction algorithm. The feature vector describing the foreground (vehicle) silhouette encodes size, aspect ratio, width, solidity in order to classify vehicle type (car, van, HGV), In addition 3D colour histograms are used to generate a feature vector encoding vehicle color. The good recognition rates achieved in the our experiments indicate that our approach is well suited for pragmatic embedded vehicle classification applications.