Sistem Pengukur Kecepatan Kendaraan Berbasis Pengolahan Video

Satrio Sani Sadewo(1*), Raden Sumiharto(2), Ika Candradewi(3)

(2) Department of Computer Science and Electronics, Universitas Gadjah Mada
(3) Department of Computer Science and Electronics, Universitas Gadjah Mada
(*) Corresponding Author


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.


video processing, speed measurement, background subtraction, gaussian mixture model, region of interest

Full Text:



[1]Raharjo, J., Susatio, E., dan Tirtoasmono, I. I., 2012, Perancangan dan Prototyping Sistem Pemantau Lalu Lintas Berbasis Video Processing dalam Mendukung Intelligent Transportation System, Prosiding InSINas, hal. 20-24, Bandung

[2] Solichin, A., dan Harjoko, A., 2013, Metode Background Subtraction untuk Deteksi Objek Pejalan Kaki pada Lingkungan Statis, Seminar Nasional Aplikasi Teknologi Informasi (SNATI), hal. B-1 – B-6, Yogyakarta.

[3] Zheng, Z., Wang, X., Zhou, G., dan Jiang, L., 2012, Vehicle Detection Based On Morphology From Highway Aerial Images, IEEE International Conference on Signal and Image Processing Application pp. 5997-6000, Sichuan, China.

[4] Braedski, G. dan Kaehler, A., 2008, Learning OpenCV, Gravenstein Highway North : O'Reilly Media, Inc.

[5] Awaludin, L., 2012, Pemrosesan Video Pendeteksi Kecepatan dan Ketinggian Aliran Lahar Dingin Pendukung Sistem Peringatan Dini, Skripsi, Jurusan Ilmu Komputer dan Elektronika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Gadjah Mada, Yogyakarta.

[6] Rohman, M., 2011, Analisa Gerakan Tangan Manusia pada Video Digital, Skripsi, Jurusan Teknik Elektro, Fakultas teknik Industri, Institut Teknologi Sepuluh November, Surabaya.


Article Metrics

Abstract views : 3242 | views : 3498


  • There are currently no refbacks.

Copyright (c) 2015 IJEIS - Indonesian Journal of Electronics and Instrumentation Systems

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Copyright of :
IJEIS (Indonesian Journal of Electronics and Instrumentations Systems)
ISSN 2088-3714 (print); ISSN 2460-7681 (online)
is a scientific journal the results of Electronics
and Instrumentations Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133 |

View My Stats1
View My Stats2