Study on automatic detection and recognition algorithms for vehicles and license plates using LS-SVM

Author

Ge, Guangying ; Bao, Xinzong ; Ge, Jing

Author_Institution

Liaocheng Univ., Liaocheng

fYear

2008

fDate

25-27 June 2008

Firstpage

3760

Lastpage

3765

Abstract

Based on pattern recognition theory and least squares support vector machine(LS-SVM) technology, automatic detection, location, segmentation and recognition of vehicles and license plates characters are discussed. A new multi-sorts classification method-binary exponent classification is proposed. By comparing LS-SVM with BP neural network in vehicle and license plates pattern recognition and classification. Experimental results showed that SVM improve recognition rate and can avoid the problem of the local optimal solution of BP network, and therefore has more practicability.

Keywords

backpropagation; character recognition; image classification; image segmentation; neural nets; road vehicles; support vector machines; BP neural network; automatic detection algorithms; binary exponent classification; least squares support vector machine; license plates characters recognition; license plates characters segmentation; multisorts classification method; pattern recognition theory; vehicle recognition; Automation; Character recognition; Intelligent control; Least squares methods; Licenses; Pattern recognition; Support vector machine classification; Support vector machines; Vehicle detection; Vehicles; Least Squares Support Vector Machines (LS-SVM); Vehicles and License Plates detection and recognition; binary exponent classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on

Conference_Location

Chongqing

Print_ISBN

978-1-4244-2113-8

Electronic_ISBN

978-1-4244-2114-5

Type

conf

DOI

10.1109/WCICA.2008.4593528

Filename

4593528