DocumentCode :
190878
Title :
A sparse representation method for traffic sign recognition based on similar class
Author :
Lei Yi ; Chong-yang Zhang
Author_Institution :
Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol., Nanjing, China
fYear :
2014
fDate :
5-8 Aug. 2014
Firstpage :
262
Lastpage :
266
Abstract :
In this paper, we propose a sparse representation method for traffic sign recognition based on similar class. The method needs to presort traffic signs as four main class according to its similar feature. We named the four main class as speed-limiting class, warning class, directive class and no-rules class. Then the method can be divided into two phases. First, we use a combination of PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) method to determine `the nearest neighbor´ for the test sample. Second, we represent the test sample as a linear combination of the training samples from the main class that `the nearest neighbor´ belongs to. Then we use the representation result to perform classification. Comparative experiments on German traffic signs database (GTSDB) show that the method is better than traditional methods such as OMP, PCA and LDA. Its recognition rate can reach 96%.
Keywords :
image representation; object recognition; principal component analysis; traffic engineering computing; GTSDB; German traffic signs database; LDA; OMP; PCA; directive class; linear discriminant analysis; no-rules class; orthogonal matching pursuit; principal component analysis; sparse representation method; speed-limiting class; traffic sign recognition; warning class; Classification algorithms; Kernel; Matching pursuit algorithms; Principal component analysis; Signal processing algorithms; Training; Transforms; linear combination; similar class; sparse representation; traffic sign recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing, Communications and Computing (ICSPCC), 2014 IEEE International Conference on
Conference_Location :
Guilin
Print_ISBN :
978-1-4799-5272-4
Type :
conf
DOI :
10.1109/ICSPCC.2014.6986195
Filename :
6986195
Link To Document :
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