Urban vehicle classification based on linear SVM with efficient vector sparse coding

Abstract

This paper presents a new method to solve the urban vehicle classification problem by incorporating an efficient vector sparse coding technique with the linear support vector machine (SVM) classifier. Essentially, SIFT descriptors are able to give good local characteristics of a vehicle image. However, in general, SIFT feature vectors are nonlinearly discriminated. With sparse coding, the SIFT feature vectors can be firstly projected to a higher dimensional feature domain where the resultant sparse code vectors may be more distinguishable than those in original feature domain and thus the linear SVM classifier can be adopted. Conventional vector sparse coding is computationally expensive which reduces the practical value of sparse coding for real vehicle classification applications. In this paper, an efficient L2-norm constraint based vector sparse coding algorithm for vehicle classification has been formulated and derived accordingly. The performance evaluations using real vehicle images extracted from surveillance video data are carried out and six vehicle classes (bus, truck, SUV, van, car, and motorcycle) are considered. Experimental results validate the effectiveness of the proposed method and it is encouraged to see that a good classification performance is achieved.