Title :
Identification of Degraded Traffic Sign Symbols Using Multi-class Support Vector Machines
Author :
Li, Lunbo ; Ma, Guangfu ; Ding, Shuyan
Author_Institution :
Harbin Inst. of Technol., Harbin
Abstract :
We present a novel classification method for recognizing traffic sign symbols undergoing image degradations. In order to cope with the degradations, it is desirable to use combined blur-affine invariants (CBAIs) of traffic sign symbols as the feature vectors. Combined invariants allow to recognize objects in the degraded scene without any restoration. In this research, multi-class support vector machines (M-SVMs) is applied to traffic sign classification and compared with backpropagation (BP) algorithm, which has been commonly used in neural network. Experimental results indicate that M-SVMs algorithm is superior to BP algorithm both on the classification accuracy and generalization performance of the classifier.
Keywords :
affine transforms; backpropagation; generalisation (artificial intelligence); image classification; image restoration; neural nets; object recognition; support vector machines; traffic engineering computing; backpropagation algorithm; classification method; combined blur-affine invariants; degraded traffic sign symbols; feature vectors; generalization performance; image degradations; image restoration; multiclass support vector machines; neural network; object recognition; traffic sign classification; Automation; Backpropagation algorithms; Degradation; Image edge detection; Learning systems; Mechatronics; Neural networks; Support vector machine classification; Support vector machines; Telecommunication traffic; Traffic signs identification; combined blur-affine invariants; degraded image; multi-class support vector machines;
Conference_Titel :
Mechatronics and Automation, 2007. ICMA 2007. International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-0828-3
Electronic_ISBN :
978-1-4244-0828-3
DOI :
10.1109/ICMA.2007.4303943
