Multi-traffic objects classification using support vector machine

Author

Sheng, Neng ; Wang, Hui ; Liu, Hong

Author_Institution

Dept. of Control Sci. & Eng., Zhejiang Univ., Hangzhou, China

fYear

2010

fDate

26-28 May 2010

Firstpage

3215

Lastpage

3218

Abstract

In order to classify the traffic objects in multi-traffic scenes, six classes were divided firstly, then eight features base on shape and motion information are extracted. The eight features of traffic objects will be the input of the support vector machine (SVM) classifier which is contrasted with RBF neural network classifier. The object type is classified according to the output of the SVM. Experimental results based on actually scene video indicate that the algorithm could classify the traffic objects in multi-traffic scenes at a high recognition ratio.

Keywords

feature extraction; image motion analysis; object recognition; radial basis function networks; road traffic; support vector machines; traffic engineering computing; RBF neural network classifier; feature extraction; high recognition ratio; motion information; multitraffic scenes; shape information; support vector machine; traffic object classification; Bicycles; Discrete wavelet transforms; Fast Fourier transforms; Feature extraction; Layout; Shape; Support vector machine classification; Support vector machines; Telecommunication traffic; Vehicles; Classification; Multi traffic; Support Vector Machine (SVM);

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2010 Chinese

Conference_Location

Xuzhou

Print_ISBN

978-1-4244-5181-4

Electronic_ISBN

978-1-4244-5182-1

Type

conf

DOI

10.1109/CCDC.2010.5498606

Filename

5498606