DocumentCode :
535016
Title :
Vision detection of vehicle occupant classification with Legendre moments and Support Vector Machine
Author :
Gao, Zhenhai ; Duan, Lifei
Author_Institution :
State Key Lab. of Automobile Dynamics Simulation, Jilin Univ., Changchun, China
Volume :
4
fYear :
2010
fDate :
16-18 Oct. 2010
Firstpage :
1979
Lastpage :
1983
Abstract :
Intelligent airbag system can control its deploy time and force according to different types of occupant in different sitting position. The accurate detection of vehicle occupant is the precondition and plays an important role in such system. This paper presents a vision detection method using low-cost CMOS camera and pattern recognition algorithm for the classification of different occupant classes. First, in view of the relative fixed passenger room, pre-processing algorithm and occupant edge detection method for the measurement space of occupant image are established; then, principal feature of occupant image edge in the measurement space is described using Legendre moments; at last, classifier of vehicle occupant is obtained using Support Vector Machine so that the classification of vehicle occupant is made possible.
Keywords :
CMOS image sensors; Legendre polynomials; cameras; edge detection; image classification; object detection; support vector machines; traffic engineering computing; CMOS camera; Legendre moment; intelligent airbag system; occupant edge detection; occupant image edge; pattern recognition; preprocessing algorithm; support vector machine; vehicle occupant classification; vision detection; Cameras; Classification algorithms; Extraterrestrial measurements; Feature extraction; Image edge detection; Support vector machines; Vehicles; Legendre moments; intelligent airbag system; occupant classification; support vector machine; vision detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing (CISP), 2010 3rd International Congress on
Conference_Location :
Yantai
Print_ISBN :
978-1-4244-6513-2
Type :
conf
DOI :
10.1109/CISP.2010.5646680
Filename :
5646680
Link To Document :
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