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An overview of traffic sign detection and classification methods

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Abstract

Over the last few years, different traffic sign recognition systems were proposed. The present paper introduces an overview of some recent and efficient methods in the traffic sign detection and classification. Indeed, the main goal of detection methods is localizing regions of interest containing traffic sign, and we divide detection methods into three main categories: color-based (classified according to the color space), shape-based, and learning-based methods (including deep learning). In addition, we also divide classification methods into two categories: learning methods based on hand-crafted features (HOG, LBP, SIFT, SURF, BRISK) and deep learning methods. For easy reference, the different detection and classification methods are summarized in tables along with the different datasets. Furthermore, future research directions and recommendations are given in order to boost TSR’s performance.

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  1. LaSTIC Laboratory, Department of Computer Science, University of Batna 2, Batna, Algeria

    Yassmina Saadna & Ali Behloul

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  1. Yassmina Saadna
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Correspondence to Yassmina Saadna.

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Saadna, Y., Behloul, A. An overview of traffic sign detection and classification methods. Int J Multimed Info Retr 6, 193–210 (2017). https://doi.org/10.1007/s13735-017-0129-8

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