Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
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Abstract
Advance Driving Assistance System (ADAS) as a standard safety feature in modern vehicles is one of the most developed transportation technologies. The ADAS itself is built by several subsystems, one of which is the detection and recognition of traffic signs. This study presents a system of detection and recognition of the speed limit traffic signs on the roadside with certain conditions. The process of detecting traffic signs using HOG (Histogram of Oriented Gradient) as a feature of image and classified them using SVM (Support Vector Machine) method. With the detection and recognition system of traffic signs, it is expected to improve the component of ADAS. The output of this system is information about the allowed speed limits on the road based on detected and recognizable sign. Test result shows the system yields accuracy more than 80% for detection and recognition.
How to cite this article:
Yosua Pangihutan Sagala, Agus Virgono and Randy Erfa Saputra. Speed Sign Detection and Recognition using Histogram of Oriented Gradient and
Support Vector Machine Method on Raspberry Pi.
DOI: https://doi.org/10.36478/jeasci.2019.7495.7501
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2019.7495.7501