Sistem Pengukur Kecepatan Kendaraan Berbasis Pengolahan Video

This system is implemented by digital image processing to detect the objects and measure the speed. This system using background subtraction method with Gaussian Mixture Model (GMM) algorithm. Background subtraction will separate background and detected objects. Coordinates of the objects midpoint u...

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Bibliographic Details
Main Authors: Satrio Sani Sadewo, Raden Sumiharto, Ika Candradewi
Format: Article
Language:Indonesian
Published: Universitas Gadjah Mada 2015-10-01
Series:IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)
Subjects:
Online Access:https://jurnal.ugm.ac.id/ijeis/article/view/7641
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spelling doaj-ca673838354b4a268487513ef7df87cd2020-11-25T01:33:14ZindUniversitas Gadjah MadaIJEIS (Indonesian Journal of Electronics and Instrumentation Systems)2088-37142460-76812015-10-015217718610.22146/ijeis.76416405Sistem Pengukur Kecepatan Kendaraan Berbasis Pengolahan VideoSatrio Sani SadewoRaden SumihartoIka CandradewiThis system is implemented by digital image processing to detect the objects and measure the speed. This system using background subtraction method with Gaussian Mixture Model (GMM) algorithm. Background subtraction will separate background and detected objects. Coordinates of the objects midpoint used as the the object moving value in pixel. The actual distance also measured in meters where the distance is limited by region of interest (ROI). The ROI is 160 pixel. Having obtained the moving objects time from previous frame to current frame so the value of pixel/s can converted to km/h. System testing the measurement validation, calculate the speed after being validated, and the influence of light intensity. The speed validation process uses average speed of early three frames speed as the reference for the speed measurement in the next frame. The average speed accuracy of 3 frames early gives a percentage error about 1,92% - 15,75%. When validation is performed on the entire reading frame of video, it produces an error range 1,21% - 21,37%. The system works well in the morning, afternoon, and evening conditions with light intensity about 600-1900 lux. While at night with 0-5 lux light intensity range, the system can’t work properly.https://jurnal.ugm.ac.id/ijeis/article/view/7641video processing, speed measurement, background subtraction, gaussian mixture model, region of interest
collection DOAJ
language Indonesian
format Article
sources DOAJ
author Satrio Sani Sadewo
Raden Sumiharto
Ika Candradewi
spellingShingle Satrio Sani Sadewo
Raden Sumiharto
Ika Candradewi
Sistem Pengukur Kecepatan Kendaraan Berbasis Pengolahan Video
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)
video processing, speed measurement, background subtraction, gaussian mixture model, region of interest
author_facet Satrio Sani Sadewo
Raden Sumiharto
Ika Candradewi
author_sort Satrio Sani Sadewo
title Sistem Pengukur Kecepatan Kendaraan Berbasis Pengolahan Video
title_short Sistem Pengukur Kecepatan Kendaraan Berbasis Pengolahan Video
title_full Sistem Pengukur Kecepatan Kendaraan Berbasis Pengolahan Video
title_fullStr Sistem Pengukur Kecepatan Kendaraan Berbasis Pengolahan Video
title_full_unstemmed Sistem Pengukur Kecepatan Kendaraan Berbasis Pengolahan Video
title_sort sistem pengukur kecepatan kendaraan berbasis pengolahan video
publisher Universitas Gadjah Mada
series IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)
issn 2088-3714
2460-7681
publishDate 2015-10-01
description This system is implemented by digital image processing to detect the objects and measure the speed. This system using background subtraction method with Gaussian Mixture Model (GMM) algorithm. Background subtraction will separate background and detected objects. Coordinates of the objects midpoint used as the the object moving value in pixel. The actual distance also measured in meters where the distance is limited by region of interest (ROI). The ROI is 160 pixel. Having obtained the moving objects time from previous frame to current frame so the value of pixel/s can converted to km/h. System testing the measurement validation, calculate the speed after being validated, and the influence of light intensity. The speed validation process uses average speed of early three frames speed as the reference for the speed measurement in the next frame. The average speed accuracy of 3 frames early gives a percentage error about 1,92% - 15,75%. When validation is performed on the entire reading frame of video, it produces an error range 1,21% - 21,37%. The system works well in the morning, afternoon, and evening conditions with light intensity about 600-1900 lux. While at night with 0-5 lux light intensity range, the system can’t work properly.
topic video processing, speed measurement, background subtraction, gaussian mixture model, region of interest
url https://jurnal.ugm.ac.id/ijeis/article/view/7641
work_keys_str_mv AT satriosanisadewo sistempengukurkecepatankendaraanberbasispengolahanvideo
AT radensumiharto sistempengukurkecepatankendaraanberbasispengolahanvideo
AT ikacandradewi sistempengukurkecepatankendaraanberbasispengolahanvideo
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