An improved vehicle classification method based on Gabor features

Authors:
Ying-nan Zhao;Zheng-dong Liu;Jing-yu Yang
Affiliations:
Computer Department, Nanjing University of Science & Technology, Nanjing, China;Computer Department, Nanjing University of Science & Technology, Nanjing, China;Computer Department, Nanjing University of Science & Technology, Nanjing, China
Venue:
Intelligent information processing II
Year:
2004

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Abstract

Vehicle classification is an important issue in the domain of ITS (Intelligent Transportation Systems). In this paper we presents an improved one based on Gabor features, which contains three consecutive stages: vehicle segmentation, Gabor features extraction and template matching. A novel non-even sampling of Gabor features is proposed. The experimental data show that this method can heavily reduce the computation and memory requirements, and illustrate good performance both in discrimination ability and robustness.