A new sparse model for traffic sign classification using soft histogram of oriented gradients

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• 作者：Peyman Hosseinzadeh Kassani ; Andrew Beng Jin Teoh ; ^{bjteoh@yonsei.ac.kr} ; ^{andrew_tbj@yahoo.com}
• 关键词：Traffic sign classification ; Multivariate polynomial model ; Sparse bayesian learning ; Relevance vector machine
• 刊名：Applied Soft Computing
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：52
• 期：Complete
• 页码：231-246
• 全文大小：2560 K
• 卷排序：52

文摘

Proposed a compact yet discriminative feature dedicated to traffic sign recognition problem. Introduced two sparse analytical non-linear classifiers for joint feature selection and classification. The performance of two sparse classifiers is no longer sensitive to the polynomial order and less prone to overfitting. Proposed sparse learners reduce the storage complexity and testing time. With smaller feature size the proposed model can get higher classification accuracy. Verified the model reliability using extensive experiments on several data sets.