Abstract
This paper applies the updated derived kernel algorithm into the vehicle type recognition, which is a heated research area based on the method of pattern recognition and digital image processing because of its significant usage on exit and entry administration, traffic and vehicle control and toll collection. The method of two- layer derived kernel on neural response is involved in extracting useful features from vehicle images, for the method itself has better capacity of decreasing the negative influences from different colors and vehicle speeds, background condition interference and blur noises. Some clustering algorithms are employed on the process of templates construction, and the first nearest neighbor algorithm on pattern classification. Since our method can get rid of the disturbances from similar parts of vehicle images and make the most of the features of representative parts, the vehicle type recognition accuracy reaches above 95% as high.
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Zhang, Z.C., Tang, Y.Y., Liu, C.S., Ding, X.Q. (2012). The Derived Kernel Based Recognition Method of Vehicle Type. In: Huang, R., Ghorbani, A.A., Pasi, G., Yamaguchi, T., Yen, N.Y., Jin, B. (eds) Active Media Technology. AMT 2012. Lecture Notes in Computer Science, vol 7669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35236-2_30
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DOI: https://doi.org/10.1007/978-3-642-35236-2_30
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