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Embedded Real-Time System for Traffic Sign Recognition on ARM Processor

Embedded Real-Time System for Traffic Sign Recognition on ARM Processor

Hassene Faiedh, Wajdi Farhat, Sabrine Hamdi, Chokri Souani
Copyright: © 2020 |Volume: 11 |Issue: 2 |Pages: 22
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799802853|DOI: 10.4018/IJAMC.2020040104
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MLA

Faiedh, Hassene, et al. "Embedded Real-Time System for Traffic Sign Recognition on ARM Processor." IJAMC vol.11, no.2 2020: pp.77-98. http://doi.org/10.4018/IJAMC.2020040104

APA

Faiedh, H., Farhat, W., Hamdi, S., & Souani, C. (2020). Embedded Real-Time System for Traffic Sign Recognition on ARM Processor. International Journal of Applied Metaheuristic Computing (IJAMC), 11(2), 77-98. http://doi.org/10.4018/IJAMC.2020040104

Chicago

Faiedh, Hassene, et al. "Embedded Real-Time System for Traffic Sign Recognition on ARM Processor," International Journal of Applied Metaheuristic Computing (IJAMC) 11, no.2: 77-98. http://doi.org/10.4018/IJAMC.2020040104

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Abstract

This article proposes the design of a novel hardware embedded system used for automatic real-time road sign recognition. The algorithm used was implemented in two main steps. The first step, which detects the road signs, is performed by the maximally stable extremal region method on HSV color space. The second step enables the recognition of the detected signs by using the oriented fast and rotated brief features method. The novelty of the embedded hardware system, on an ARM processor, leads to a real-time implementation of the ADAS applications. The proposed system was tested on the Belgium Traffic Sign Detection and Recognition Benchmark and on the German Traffic Signs Datasets. The proposed approach attained a high detection and recognition rate with real-world situations. The achieved results are acceptable when compared to state-of-the-art systems.

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