Pengembangan Background Subtraction Menggunakan FCM Untuk Deteksi Objek Bergerak Berdasarkan Pencahayaan Yang Bervariasi

Authors

  • Rama Aria Megantara Dian Nuswantoro University
  • Ricardus Anggi Pramunendar Dian Nuswantoro University

DOI:

https://doi.org/10.33633/tc.v16i4.1541

Keywords:

video, background subtraction, algoritma OTSU, adaptive threshold, FCM (Fuzzy C-Means)

Abstract

Pendataan dari video yang direkam pada waktu malam hari memiliki tingkat kesulitan yang lebih tinggi daripada waktu pagi atau siang hari. Perubahan pencahayaan yang dihasilkan dapat mempengaruhi kualitas gambar dari rekaman video yang dihasilkan. Sehingga pengaruh pencahayaan pada saat malam hari menghasilkan kualitas rekaman video yang sangat rendah, hal ini disebabkan karena pencahayaan pada malam hari sering mengalami perubahan secara drastis. Beberapa metode yang sering digunakan dalam menyelesaikan masalah pelacakan objek bergerak antara lain background subtraction dan algoritma OTSU. Dalam menentukan threshold, algoritma OTSU tidak dapat mendeteksi gambar secara optimal saat berhubungan dengan gambar lain dilevel abu-abu. Dengan mengusulkan algoritma adaptive threshold yang didapatkan dari algoritma FCM diharapkan dapat meningkatkan akurasi untuk mendeteksi objek bergerak pada pencahayaan yang bervarisi. Sehingga dapat dilakukan penelitian ke depan untuk analisis cerdas dalam melacak pola dan deteksi perilaku anomali oleh kendaraan di jalan

References

M. Toumi, A. Maizate, M. Ouzzif, and M. Said Salah, “Dynamic Clustering Algorithm for Tracking Targets with High and Variable Celerity (ATHVC),†J. Comput. Networks Commun., vol. 2016, pp. 1–10, 2016.

M. A. Soeleman, M. Hariadi, and M. H. Purnomo, “Adaptive threshold for background subtraction in moving object detection using Fuzzy C-Means clustering,†in TENCON 2012 IEEE Region 10 Conference, 2012, pp. 1–5.

R. S. Basuki, M. A. Soeleman, R. A. Pramunendar, A. F. Yogananti, and C. Supriyanto, “Video Object Segmentation Applying Spectral,†J. Theor. Appl. Inf. Technol., vol. 72, no. 2, pp. 208–214, 2015.

N. Suzuki, K. Hirasawa, K. Tanaka, Y. Kobayashi, Y. Sato, and Y. Fujino, “Learning motion patterns and anomaly detection by Human trajectory analysis,†2007 IEEE Int. Conf. Syst. Man Cybern., pp. 498–503, 2007.

W. Abd-Almageed, M. Hussein, M. Abdelkader, and L. S. Davis, “Real-Time Human Detection and Tracking from Mobile Vehicles,†in 2007 IEEE Intelligent Transportation Systems Conference, 2007, pp. 149–154.

K. R. Reddy, K. H. Priya, and N. Neelima, “Object Detection and Tracking -- A Survey,†in 2015 International Conference on Computational Intelligence and Communication Networks (CICN), 2015, pp. 418–421.

D. Walther, D. R. Edgington, and C. Koch, “Detection and tracking of objects in underwater video,†Proc. 2004 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition, 2004. CVPR 2004., vol. 1, pp. 0–5, 2004.

Meng-Che Chuang, Jenq-Neng Hwang, K. Williams, and R. Towler, “Tracking Live Fish From Low-Contrast and Low-Frame-Rate Stereo Videos,†IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 1, pp. 167–179, Jan. 2015.

Y. K. Wang and C. T. Fan, “Moving object detection for night surveillance,†Proc. - 2010 6th Int. Conf. Intell. Inf. Hiding Multimed. Signal Process. IIHMSP 2010, pp. 236–239, 2010.

V. Mahadevan and N. Vasconcelos, “Background subtraction in highly dynamic scenes,†in 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1–6.

R. S. Basuki, M. Hariadi, and R. A. Pramunendar, “Fuzzy C-Means Algorithm for Adaptive Threshold on Alpha Matting,†in Citee, 2012, no. July, pp. 177–180.

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Published

2017-09-09

Issue

Vol. 16 No. 4 (2017): November 2017

Section

Articles

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Copyright (c) 2017 Rama Aria Megantara, Ricardus Anggi Pramunendar

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