DocumentCode :
501414
Title :
Vehicle Types Recognition Based on Neural Network
Author :
Zhang, Xin-Bo ; Jiang, Li
Author_Institution :
Coll. of Inf. & Electron. Eng., ZheJiang Gongshang Univ., Hangzhou, China
Volume :
1
fYear :
2009
fDate :
6-7 June 2009
Firstpage :
3
Lastpage :
6
Abstract :
As a key technology of intelligent transportation system, higher identification accuracy, robustness and real-time are needed in vehicle recognition. Therefore, in view of the features of vehicle types, this paper proposes a BP neural network car types classifier method based on fuzzy C-means clustering. First, on the basis of the pretreatment of the images of the vehicle, we abstract the features of car types from images and classify the massive data by fuzzy C- means clustering algorithm. Then, design the BP neural networks to train and test the classified data. Finally it is carried on compressive judgment by the computer. Experiments prove the validity of the classifier. It can recognize the highway vehicle types rapidly.
Keywords :
backpropagation; image recognition; traffic engineering computing; vehicles; BP neural network; fuzzy C-means clustering; intelligent transportation system; vehicle types recognition; Clustering algorithms; Fuzzy neural networks; Image coding; Intelligent transportation systems; Intelligent vehicles; Neural networks; Real time systems; Road transportation; Robustness; Testing; BP algorithm; character abstraction; fuzzy c-means clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3645-3
Type :
conf
DOI :
10.1109/CINC.2009.146
Filename :
5231735
Link To Document :
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