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2018 | OriginalPaper | Chapter

Traffic Sign Classification Base on Latent Dirichlet Allocation

Authors : Lei Song, Zheyuan Liu, Xiaoteng Zhang, Huixian Duan, Na Liu, Jie Dai

Published in: Frontier Computing

Publisher: Springer Singapore

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Abstract

Traffic sign classification is a significant issue in the intelligent vehicle domain, which helps vehicles to follow the traffic rules and ensure the safety. Feature selection and description are very important and difficult for classification. In this paper, a novel traffic sign classification method is proposed which is based on the Latent Dirichlet Allocation (LDA) model. Feature topics are modeled based on various traffic signs by the LDA automatically. And traffic signs captured onboard are classified according to the modeled features. The experiment results show the efficiency of our work.

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Metadata
Title
Traffic Sign Classification Base on Latent Dirichlet Allocation
Authors
Lei Song
Zheyuan Liu
Xiaoteng Zhang
Huixian Duan
Na Liu
Jie Dai
Copyright Year
2018
Publisher
Springer Singapore
Book
Frontier Computing

Print ISBN: 978-981-10-7397-7

Electronic ISBN: 978-981-10-7398-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-7398-4_12

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