The Derived Kernel Based Recognition Method of Vehicle Type
Zhen Chao Zhang1; Yuan Yan Tang1; Chang Song Liu2; Xiao Qing Ding2
Conference Name8th International Conference, AMT 2012
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7669 LNCS
Conference DateDecember 4-7, 2012
Conference PlaceMacau
PublisherSpringer Heidelberg Dordrecht London New York

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. 

KeywordClassification Derived Kernel Neural Response Templates Construction Vehicle Type Recognition
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Department of Computer and Information Science, Faculty of Science and TechnologyUniversity of MacauMacauChina
2.Department of Electronic EngineeringTsinghua University, State Key Laboratory of Intelligent Technology and Systems BeijingChina
First Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Zhen Chao Zhang,Yuan Yan Tang,Chang Song Liu,et al. The Derived Kernel Based Recognition Method of Vehicle Type[C]:Springer Heidelberg Dordrecht London New York,2012:297-306.
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