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