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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 5019592, 14 pages
Research Article

Vehicle Type Recognition Combining Global and Local Features via Two-Stage Classification

1School of Information and Control, Nanjing University of Information Science & Technology, Nanjing 210044, China
2Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing 210044, China
3School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
4School of Electronic and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

Correspondence should be addressed to Wei Sun

Received 22 August 2017; Revised 8 October 2017; Accepted 17 October 2017; Published 13 November 2017

Academic Editor: Yakov Strelniker

Copyright © 2017 Wei Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study proposes a new vehicle type recognition method that combines global and local features via a two-stage classification. To extract the continuous and complete global feature, an improved Canny edge detection algorithm with smooth filtering and non-maxima suppression abilities is proposed. To extract the local feature from four partitioned key patches, a set of Gabor wavelet kernels with five scales and eight orientations is introduced. Different from the single-stage classification, where all features are incorporated into one classifier simultaneously, the proposed two-stage classification strategy leverages two types of features and classifiers. In the first stage, the preliminary recognition of large vehicle or small vehicle is conducted based on the global feature via a -nearest neighbor probability classifier. Based on the preliminary result, the specific recognition of bus, truck, van, or sedan is achieved based on the local feature via a discriminative sparse representation based classifier. We experiment with the proposed method on the public and established datasets involving various challenging cases, such as partial occlusion, poor illumination, and scale variation. Experimental results show that the proposed method outperforms existing state-of-the-art methods.