Implementasi Algoritma SIFT (Scale-Invariant Feature Transform) Dan Algoritma Kalman Filter Dalam Mendeteksi Objek Bola

chindra saputra

doi:10.33998/processor.2023.18.1.791

Authors

chindra saputra unama

DOI:

https://doi.org/10.33998/processor.2023.18.1.791

Abstract

The Indonesian Football Robot Contest (KRSBI) is a division of the Indonesian Robot Contest (KRI) which is held annually by RISTEKDIKTI and KEMENDIKBUD. In a soccer robot contest, the robot is required to detect the ball and then lead it to the opponent's goal so that a goal can be created. a good object detection system must be fast, light, and of course it must have good accuracy. Currently, the object detection system that has been applied to robots is in the form of color filtering which is considered quite good in detecting an object. However, if you only rely on this method, it is still lacking when viewed from the point of view of object tracking. The Kalman Filter algorithm serves as an estimator that can be used to predict the direction of movement of an object based on the object's status from the previous frame. This allows the robot to move 1 (one) frame faster than the object to be tracked. The SIFT algorithm can compare two images through the features of the image and produce whether the images are similar or not. This algorithm is useful for ascertaining whether the object detected by the robot is a ball or not. This research combines the SIFT and Kalmal Fiter algorithms to produce a better detection system than before. It is hoped that the results of this research can be used by robots in participating in the Indonesian soccer robot contest.

Downloads

Download data is not yet available.

References

S. Arifin and E. T. Wijaya, “Implementasi Teknologi Computer Vision Sebagai Pengendali Mobile Robot Berbasis Kamera Web,” Jouticla, vol. 2, no. 2, pp. 75–80, 2017, doi: 10.30736/jti.v2i2.72.

F. B. Setiawan, O. J. Aldo Wijaya, L. H. Pratomo, and S. Riyadi, “Sistem Navigasi Automated Guided Vehicle Berbasis Computer Vision dan Implementasi pada Raspberry Pi,” J. Rekayasa Elektr., vol. 17, no. 1, pp. 7–14, 2021, doi: 10.17529/jre.v17i1.18087.

R. Lionnie and M. Alaydrus, “Sistem Pendeteksi Gambar Termanipulasi Menggunakan Metode SIFT,” Techné J. Ilm. Elektrotek., vol. 16, no. 02, pp. 133–140, 2017, doi: 10.31358/techne.v16i02.166.

H. Vazirani, A. Kautsar, J. Fisika, F. Sains, and U. Diponegoro, “IMPLEMENTASI OBJECT TRACKING UNTUK MENDETEKSI DAN MENGGUNAKAN METODE KALMAN FILTER DAN GAUSSIAN MIXTURE MODEL,” vol. 5, no. 1, 2016.

P. Tanpa, A. Menggunakan, P. S. Ardiantara, R. Sumiharto, and S. B. Wibowo, “Purwarupa Kontrol Kestabilan Posisi dan Sikap pada Pesawat Tanpa Awak Menggunakan IMU dan Algoritma Fusion Sensor Kalman Filter,” vol. 4, no. 1, pp. 25–34, 2014.

M. D. Irawan and S. A. Simargolang, “Implementasi E-Arsip Pada Program Studi Teknik Informatika,” vol. 2, no. 1, 2018.

D. A. Prabowo and D. Abdullah, “Deteksi dan Perhitungan Objek Berdasarkan Warna Menggunakan Color Object Tracking,” Pseudocode, vol. 5, no. 2, pp. 85–91, 2018, doi: 10.33369/pseudocode.5.2.85-91.

Ikhsan and P. Ayomi, “Implementasi Raspebrry PI Pada ARM Robot Penyortir Benda Berdasarkan Warna dan Bnetuk,” vol. 6, no. 2, pp. 176–182, 2019.

M. Fuadi, U. Darusalam, and A. K. Whardana, “FACE RECOGNITION MENGGUNAKAN OPENCV DENGAN BAHASA PEMOGRAMAN PYTHON OOP UNTUK SISTEM,” vol. 2, no. 3, pp. 218–224, 2021.

P. Dlib, “Pendeteksian Kantuk Secara Real Time Menggunakan Pustaka OPENCV dan DLIB PYTHON Real Time Sleepiness Detection Using OPENCV Library and PYTHON DLIB,” vol. 28, no. 2, pp. 22–26, 2018.

D. Ayu and B. Utami, “Perancangan Sistem Login Pada Aplikasi Berbasis GUI Menggunakan QTDesigner Python,” vol. 4, no. 2, pp. 92–100, 2021.

A. N. Syahrudin and T. Kurniawan, “Input dan output pada bahasa pemrograman python,” no. January, 2020.

A. T. Khomaeni, “PENERAPAN METODE SCALE INVARIANT FEATURE TRANSFORM (SIFT) PADA AUGMENTED REALITY DALAM PENGENALAN GEDUNG UNIVERSITAS ISLAM NEGERI MALANG Oleh,” 2020.

R. Y. Amrullah, “Pengenalan Benda di Jalan Raya dengan Metode Kalman Filter,” 2015.

J. Ali, “SISTEM SECURITY WEBCAM DENGAN MENGGUNAKAN MICROSOFT VISUAL BASIC (6.0),” vol. 1, no. 2, pp. 48–60, 2016.