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.
Chapter PDF
Similar content being viewed by others
References
Jolly M.P.D., Lakshmanan S. and Jain A. K., Vehicle segmentation and classification using deformable templates, IEEE Transaction on Pattern Analysis and Intelligence, 1996, 18(3): 293–308
Lim T.R and Guntoro A.T. Thiang, Car recognition using gabor filter feature extraction, Circuits and Systems, APCCAS’02, 2002, 2: 451–455
Quen-Zong Wu and Bor-Shenn Jeng, Background subtraction based on logarithmic intensities, Pattern Recognition Letters, 2002, 23: 1529–1536
Chui, C.K., An Introduction to Wavelets, Academic Press, 1992
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 International Federation for Information Processing
About this paper
Cite this paper
Zhao, Yn., Liu, Zd., Yang, Jy. (2005). An Improved Vehicle Classification Method Based on Gabor Features. In: Shi, Z., He, Q. (eds) Intelligent Information Processing II. IIP 2004. IFIP International Federation for Information Processing, vol 163. Springer, Boston, MA. https://doi.org/10.1007/0-387-23152-8_61
Download citation
DOI: https://doi.org/10.1007/0-387-23152-8_61
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-23151-8
Online ISBN: 978-0-387-23152-5
eBook Packages: Computer ScienceComputer Science (R0)