Hybrid Features based on Zernike Moments and Single Value Decomposition (SVD) for Vehicle Classification System

Abstract

Vehicle identification and classification in a traffic environment containing mixed background is an important application of machine vision. The goal of this paper is to build a classifier for vehicle identification using hybrid features. The proposed work is to identify a "car" from a "non-car" amidst the mixed background taken from University of Illinois at Urbana-Champaign (UIUC) standard database. UIUC image database contains mixer of car and non-car image database. Every image is divided into equal sized small sub-block images. The Zernike features and Singular Value Decomposition (SVD) features are extracted from each sub-block of the image. The features of the vehicle objects are fed to the artificial neural classifier after normalization. The performance of the classifier is compared with various literature methods of similar work. Quantitative evaluation shows improved results of 95.6% compared with the literature papers. A critical evaluation of this approach under the proposed standards is presented.