NDLI: A sparse representation method for traffic sign recognition based on similar class

Content Provider	IEEE Xplore Digital Library
Author	Lei Yi Chong-yang Zhang
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol., Nanjing, China (Lei Yi) \|\| Key Lab. of Intell. Perception & Syst. for High-Dimensional Inf., Nanjing Univ. of Sci. & Technol., Nanjing, China (Chong-yang Zhang)
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%.
Starting Page	262
Ending Page	266
File Size	1121343
Page Count	5
File Format	PDF
ISBN	9781479952724
e-ISBN	9781479952748
DOI	10.1109/ICSPCC.2014.6986195
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Linear combination Matching pursuit algorithms Signal processing algorithms Transforms Sparse representation Classification algorithms Traffic sign recognition Similar class Kernel Principal component analysis
Content Type	Text
Resource Type	Article

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