Sistem Pengawasan Berbasis Deteksi Gerak Menggunakan Single Board Computer
Abstract
Monitoring is the important thing for the security of an area. Based on the monitoring, the condition of the area, events, and objects can be observed. Monitoring of an area generally uses Closed Circuit Television (CCTV). But CCTV cameras only function as passive supervisor which are unable to detect the appearance of objects. Therefore, the motion detection techniques need to be applied to detect the appearance of the objects. In this paper, a Gaussian blur and accumulative frame difference method was applied to detect the appearance of the objects. The method works by comparing the reference frame as a benchmark with the target frame which is filled by the objects. Based on the results of the test, the system is able to detect the objects that appear by raising a segmentation line on the objects. Then, the time of objects appearance will be recorded in a *.csv file and the system visualizes the appearance of the objects in time-series graphs. Objects appearance examination at a distance of 1 to 10 meters can work well during a bright conditions. However, in dark environments (less than 40 lux), the system has not been able to detect the appearance of an object because it depends on the specification of camera used by the users. Then, in testing the number of the objects, the system can detect multiple objects. However, if there are several objects that are too close, those objects will be merged as one object.
References
D.I. Ramadhan, I.P. Sari, dan L.O. Sari, “Comparison of Background Substraction, Sobel, Adaptive Motion Detection, Frame Differences, and Accumulative Differences Images on Motion Detection,” SINERGI, Vol. 22, No. 1, hal. 51–62, 2018.
P.L. Rosin dan T. Ellis, “Image Difference Threshold Strategies and Shadow Detection,” Proc. BMVC ’95, 1995, hal. 347-356.
M.I. Zul dan L.E. Nugroho, “Deteksi Gerak dengan Menggunakan Metode Frame Differences pada IP Camera,” Proceeding CITEE 2012, 2012, hal. 52–56.
M. Harry, B. Pratama, A. Hidayatno, dan A. Zahra, “Menggunakan Metode Background Subtraction dengan Algoritma Gaussian Mixture Model,” TRANSIENT, Vol. 6, No. 2, hal. 246–253, 2017.
R. Zakaria, “Smart Motion Detection: Security System Using Raspberry Pi,” J. Eng. Res. Inst., Vol. 30, hal. 1-8, 2017.
U. Ahmad, Pengolahan Citra Digital & Teknik Pemrogramannya, 1st ed., Yogyakarta, Indonesia: Graha Ilmu, 2005.
P. Gil, S. Maldonado, dan R. Gil, “Background Pixel Classi cation for Motion Detection in Video Image Sequences,” Neural Networks, Vol. 1, hal. 718–725, 2003.
D.L. DiLaura, An Introduction to the IES Lighting Handbook, 10th ed., New York, USA: IES, 2011.
© Jurnal Nasional Teknik Elektro dan Teknologi Informasi, under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License.
1.png)
