Implementasi Metode Unsupervised Learning Pada Sistem Keamanan Dengan Optimalisasi Penyimpanan Kamera IP

Implementing Unsupervised Learning Method in Security System With IP Camera Storage Optimization

  • Desta Yolanda Universitas Andalas
  • Mohammad Hafiz Hersyah Universitas Andalas
  • Eno Marozi Universitas Andalas
DOI: https://doi.org/10.29207/resti.v5i6.3552
Keywords: Face Recognition, CCTV, Unsupervised Learning, Raspberry Pi, Datasets, IP Forwarding

Abstract

Security monitoring systems using face recognition can be applied to CCTV or IP cameras. This is intended to improve the security system and make it easier for users to track criminals is theft. The experiment was carried out by detecting human faces for 24 hours using different cameras, namely an HD camera that was active during the day and a Night Vision camera that was active at night. The application of Unsupervised Learning method with the concept of an image cluster, aims to distinguish the faces of known or unknown people according to the dataset built in the Raspberry Pi 4. The user interface media of this system is a web-based application built with Python Flask and Python MySQL. This application can be accessed using the domain provided by the IP Forwarding device which can be accessed anywhere. According to the test results on optimization of storage, the system is able to save files only when a face is detected with an average file size of ± 2.28 MB for 1x24 hours of streaming. So that this storage process becomes more efficient and economical compared to the storage process for CCTV or IP cameras in general.

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References

[1] Atmoko, E. H. (2012). Membuat Sendiri CCTV Berkelas Enterprise Dengan Biaya Murah. In Yogyakarta: ANDI (1st ed.). Andi Publisher.
[2] Azikin, A. (2005). Kamera Pengawas Berbasis Open Source. In Elex Media Komputindo: Jakarta.
[3] Huang, Z., Shan, S., Wang, R., Zhang, H., Lao, S., Kuerban, A., & Chen, X. (2015). A Benchmark and Comparative Study of Video-Based Face Recognition on COX Face Database. IEEE Transactions on Image Processing, 24(12), 5967–5981. https://doi.org/10.1109/TIP.2015.2493448
[4] Sofyan, A., Wisaksono Sudiharto, D., & Wirawan Wijiutomo, C. (2018). Surveillance Embedded IP Camera with Integrated Cloud Storage and Image Deblurring Using Richardson-Lucy. 2018 International Seminar on Application for Technology of Information and Communication, 388–393. https://doi.org/10.1109/ISEMANTIC.2018.8549775
[5] Venkatesan, R., Raja, P. D. A., & Ganesh, A. B. (2016). Unsupervised Learning Based Video Surveillance System Established with Networked Cameras. In Advances in Signal Processing and Intelligent Recognition Systems (pp. 603–614). Springer. https://doi.org/10.1007/978-3-319-28658-7_51
[6] Ohaneme, C. O., Eke, J., Azubogu, A. C. O., Ifeagwu, E. N., & Ohaneme, L. C. (2012). Design and Implementation of an IP-based Security Surveillance System. International Journal of Computer Science Issues (IJCSI), 9(5), 391.
[7] Hutabarat, D. P., Patria, D., Budijono, S., & Saleh, R. (2016). Human tracking application in a certain closed area using RFID sensors and IP camera. 2016 3rd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), 11–16. https://doi.org/10.1109/ICITACEE.2016.7892401
[8] Rezkia, S. M. (2020). Kenali Algoritma Machine Learning (Annissa Widya Davita (ed.); 14th ed.). DQLab.
[9] Noor, M. H., & Hariadi, M. (2009). Image Cluster Berdasarkan Warna untuk Identifikasi Kematangan Buah Tomat dengan Metode Valley Tracing. Seminar Nasional Informatika (SEMNASIF), 1(1).
[10] Space and Naval Warfare Systems Center Atlantic. (2013). CCTV Technology Handbook. U.S. Department of Homeland Security.
[11] Roughan, M., Griffin, T., Mao, M., Greenberg, A., & Freeman, B. (2004). Combining routing and traffic data for detection of IP forwarding anomalies. ACM SIGMETRICS Performance Evaluation Review, 32(1), 416–417. https://doi.org/10.1145/1012888.1005745
[12] Ratminingsih, N. M. (2010). Penelitian Eksperimental dalam Pembelajaran Bahasa Kedua. Prasi: Jurnal Bahasa, Seni, Dan Pengajarannya, 6(11), 31–40. https://doi.org/0.23887/prasi.v6i11.6816.g4664
Published
2021-12-30
How to Cite
Desta Yolanda, Mohammad Hafiz Hersyah, & Eno Marozi. (2021). Implementasi Metode Unsupervised Learning Pada Sistem Keamanan Dengan Optimalisasi Penyimpanan Kamera IP . Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(6), 1099 - 1105. https://doi.org/10.29207/resti.v5i6.3552
Issue
Vol 5 No 6 (2021): Desember 2021
Section
Information Technology Articles

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