Adaptive Background Extraction for Video Based Traffic Counter Application Using Gaussian Mixture Models Algorithm

Raymond Sutjiadi, Endang Setyati, Resmana Lim


The big cities in the world always face the traffic jam. This problem is caused by the increasing number of vehicle from time to time and the increase of vehicle is not anticipated with the development of new road section that is adequate. One important aspect in the traffic management concept is the need of traffic density data of every road section. Therefore, the purpose of this paper is to analyze the possibility of optimization on the use of video file recorded from CCTV camera for the visual observation and the tool for counting traffic density. The used method in this paper is adaptive background extraction with Gaussian Mixture Models algorithm. It is expected to be the alternative solution to get the data of traffic density with a quite adequate accuracy as one of aspects for decision making process in the traffic engineering


traffic management system; traffic density counter; adaptive background extraction; gaussian mixture models;

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Dinas Perhubungan Kota Surabaya. Surabaya Intelligent Transport System (SITS). ( [Accessed on March 20, 2015]

Badan Penelitian dan Pengembangan Kementrian Pekerjaan Umum. PLATO: Penghitung Lalu Lintas Otomatis. Kementerian Pekerjaan Umum. 2010.

Lim R, Thiang, Rizal. Pengukuran Kepadatan Arus Lalu Lintas Menggunakan Sensor Kamera. Seminar Nasional Industrial Electronics 2003 (IES 2003). 2003.

Rostianingsih S, Adipranata R, Wibisono FS. Adaptive Background dengan Metode Gaussian Mixture Models Untuk Real-Time Tracking. Jurnal Teknik Informatika. 2008; 9(1); 68-77.

Stauffer C, Grimson WEL, Adaptive Background Mixture Models for Real-Time Tracking, Massachusetts Institute of Technology. 1999

KaewTraKulPong P, Bowden R. An Improved Adaptive Background Mixture Model for Real-Time Tracking with Shadow Detection, Proc.2nd European Workshop on Advanced Video Based Surveillance Systems. September 2001.

Kaehler A, Bradski G. Learning OpenCV: Computer Vision with the OpenCV Library, 2nd ed.. California, United States of America: O’Reilly Media, Inc. October 2013.

OpenCV. OpenCV Documentation ( [Accessed on March 20, 2015].

Wikipedia. OpenCV ( [Accessed on March 20, 2015].



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