TY  - JOUR
T1  - Speed Sign Detection and Recognition using Histogram of Oriented Gradient and
Support Vector Machine Method on Raspberry Pi
AU - Sagala, Yosua Pangihutan AU - Virgono, Agus AU - Erfa Saputra, Randy 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 20
SP  - 7495
EP  - 7501
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.7495.7501
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.7495.7501
KW  - Traffic sign detection
KW  -histogram of oriented gradient
KW  -support vector machine
KW  -ADAS
KW  -HOG
KW  -recognizable
AB  - 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.
ER  -