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Alfina Alfina
Magister Teknik Elektro Universitas Syiah Kuala
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Perancangan dan Pengujian Piranti Pemantauan Visual untuk Menentukan Volume Lalu Lintas Alfina Alfina; Yuwaldi Away; Muhammad Isya
Jurnal Rekayasa Elektrika Vol 12, No 2 (2016)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (820.134 KB) | DOI: 10.17529/jre.v12i2.4061

Abstract

Intelligent Transportation System (ITS) applies information and communication technologies for transportation management. The problem in the study is the importance of computer vision tools to determine the volume of traffic on the highway. The design of computer vision device is performed using a static camera and the bounding box method for determining the traffic volume based on vehicle type, namely light vehicles, heavy vehicles (trucks) and motorcycles that are applied on the Gaussian Mixture Models (GMM). The classification is based on the pixel area of vehicle objects. The tests were carried out at Jalan T. Nyak Arief, Jambotape, Banda Aceh. The test results of the system achieved an accuracy of 79.32%.
Perancangan dan Pengujian Piranti Pemantauan Visual untuk Menentukan Volume Lalu Lintas Alfina Alfina; Yuwaldi Away; Muhammad Isya
Jurnal Rekayasa Elektrika Vol 12, No 2 (2016)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v12i2.4061

Abstract

Intelligent Transportation System (ITS) applies information and communication technologies for transportation management. The problem in the study is the importance of computer vision tools to determine the volume of traffic on the highway. The design of computer vision device is performed using a static camera and the bounding box method for determining the traffic volume based on vehicle type, namely light vehicles, heavy vehicles (trucks) and motorcycles that are applied on the Gaussian Mixture Models (GMM). The classification is based on the pixel area of vehicle objects. The tests were carried out at Jalan T. Nyak Arief, Jambotape, Banda Aceh. The test results of the system achieved an accuracy of 79.32%.
Co-Authors Muhammad Isya Yuwaldi Away