OBJECT MOTION DETECTION BASED ON IMAGE PROCESSING USING BINARY-IMAGE COMPARISON METHOD

Main Article Content

Matalangi Matalangi
Abdul Jalil

Abstract

The purpose of this study is to build the object motion detection system based on the image processing technique used a Binary-Image Comparison (BIC) method. The function of the BIC method in this study is as a decision-maker to make the system be able to send the message data as a result of object motion detection. The object motion has detected in this study is the object with a colour of red, yellow, green and blue. In this study, the image processing segmentation has been processed using OpenCV library software and was executed in the Robot Operating System 2 (ROS2) nodes. Some of the ROS2 nodes have been used to build the object motion detection on this study, that is a node to read the RGB camera input, a node to detect the red object motion, a node to detect the yellow object motion, a node to detect the green object motion, a node to detect the blue object motion, and a node to receive the result of the motion detection object. Each node in this systems will be connected to each other through a topic to make them are able to exchange the image message data using the Data Distribution Service (DDS) protocol on the ROS2. The result from this study is the systems can be detected the object motion are red, yellow, green, and blue then sent it as a message data based on the resulting process from the BCI method.

Downloads

Download data is not yet available.

Article Details

How to Cite
Matalangi, M., & Jalil, A. (2020). OBJECT MOTION DETECTION BASED ON IMAGE PROCESSING USING BINARY-IMAGE COMPARISON METHOD. Electro Luceat, 6(1), 109-116. https://doi.org/10.32531/jelekn.v6i1.207
Section
Articles
Author Biography

Matalangi Matalangi, STMIK Handayani Makassar

Jurusan Sistem Komputer, STMIK Handayani Makassar

References

[1] Basir, B., Wardi., dan Zainuddin, Z. 2017. Sistem Keamanan Rumah Berbasis Kinect. JURNAL IT, Vol.8, No.2, hlm. 84-96.
[2] Riyadi, TA. 2017. Analisis Sistem Pemantauan Video Menggunakan IP Camera Pada Suatu Unit Usaha PTN. Jurnal Teknologi Rekayasa, Vol.22, No.2, hlm.103-112.
[3] Uktoro, AI. 2017. Analisis Citra Drone Untuk Monitoring Kesehatan Tanaman Kelapa Sawit. Jurnal Agroteknose, Vol. VIII, No.II, hlm.8-15.
[4] Ifan., Musa, MDT., Farhamsa, D. 2015. Alarm Kebakaran Berbasis Citra. Gravitasi, Vol.14, No.1, hlm.90-96.
[5] Pradana, SY., Utaminingrum, F., dan Kurniawan, W. 2018. Deteksi Titik Api Terpusat Menggunakan Kamera Dengan Notifikasi Berbasis Sms Gateway Pada Raspberry Pi. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, Vol.2, No.12, hlm.7183-7191.
[6] Jalil, A. 2018. Sistem Kontrol Mesin Penukaran Uang Kertas Rupiah Berbasis Pengolahan Citra dan Raspberry Pi. ILKOM Jurnal Ilmiah, Vol.10, No.2, hlm.128-135.
[7] Mursalim, MKN. 2018. Pendeteksian dan Pelacakan Objek Bergerak pada UAV berbasis Metode SUED. JNTETI, Vol.7, No.1, hlm.105-111.
[8] Dewi, YF dan Fadilla, N. 2019. Deteksi Objek Berwarna Merah Secara Real Time Dengan Algoritma Color Filtering. Jurnal Media Informatika Budidarma, Vol.3, No.2, hlm.140-143.
[9] Putri, NN. 2016. Aplikasi Pendeteksi Objek Bergerak Pada Image Sequence Dengan Metode Background Substraction. Jurnal Teknologi Rekayasa, Vol.21, No.3, hlm.162-172.
[10] Yuha, RA., Fikri, MDA., Ashari, Pranata, R., dan Harahap, M. 2019. Seminar Nasional APTIKOM (SEMNASTIK), hlm.503-511.
[11] Yunardi, RT., Mardhiyah, AW., Yahya, MH., dan Arisgraha. FCS. 2019. Desain dan Implementasi Visual Object Tracking Menggunakan Pan and Tilt Vision System. ELKHA, Vol.11, No. 2, hal.85-92.
[12] Prabowo, DA., Abdullah, D., dan Manik, A. 2018. Deteksi dan Perhitungan Objek Berdasarkan Warna Menggunakan Color Object Tracking. Jurnal Pseudocode, Vol.5, No.2, hal.85-91.
[13] Muwardi, F dan Fadlil, A. 2017. Sistem Pengenalan Bunga Berbasis Pengolahan Citra dan Pengklasifikasi Jarak. Jurnal Ilmu Teknik Elektro Komputer dan Informatika (JITEKI), Vol.3, No.2, hal.124-131.
[14] Kusumanto, RD dan Tompunu, AN. 2011. Pengolahan Citra Digital Untuk Mendeteksi Obyek Menggunakan Pengolahan Warna Model Normalisasi RGB. Seminar Nasional Teknologi Informasi & Komunikasi Terapan 2011 (Semantik 2011). ISBN 979-26-0255-0.
[15] Murniyasih, E dan Suryani, L. 2020. Penerapan Metode Learning Vector Quantization Untuk Identifikasi Penyakit Padi Berdasarkan Bentuk Bercak Daun. Jurnal Elektro Luceat, Vol.6, No.1.
[16] Baudrier, E., Millon, G., Nicolier, F., dan Ruan, S. 2006. A fast binary-image comparison method with local-dissimilarity quantification. International Conference on Pattern Recognition (ICPR’06). DOI: 10.1109/ICPR.2006.63.
[17] Paumard, J. 1997. Robust comparison of binary images. ELSEVIER, Pattern Recognition Letters.
[18] Mustafa, AAY. 2019. Fast Size-Invariant Binary Image Matching Through Dissimilarity via Pixel Mapping. International Journal of Engineering Research and Technology, Vol.12, No.8, hal.1293-1306.
[19] Jalil, A. 2018. Robot Operating System (ROS) dan Gazebo Sebagai Media Pembelajaran Robot Interaktif. ILKOM Jurnal Ilmiah, Vol.10, No.3, hal.284-289.
[20] Jalil, A. 2019. Pemanfaatan Middleware Robot Operating System (ROS) Dalam Menjawab Tantangan Revolusi Industri 4.0. ILKOM Jurnal Ilmiah, Vol.11, No.1, hal.45-52.
[21] Maruyama, Y., Kato, S., dan Azumi. T. 2016. Exploring the Performance of ROS2. EMSOFT.
[22] Diluoffo, V., Michalson, WR., dan Sunar, B. 2018. Robot Operating System 2: The need for a holistic security approach to robotic architectures. International Journal of Advanced Robotic Systems.
Abstract viewed = 790 times
PDF downloaded = 675 times