Low-Cost CCTV for Home Security With Face Detection Base on IoT

Authors

  • Muhammad Akbar Syahbana Pane Institut Teknologi Sawit Indonesia, Nort Sumatera, Indonesia
  • Khairul Saleh Jurusan Teknik Elektro, Sekolah Tinggi Teknologi Immanuel, Nort Sumatera, Indonesia
  • Andi Prayogi Institut Teknologi Sawit Indonesia, Nort Sumatera, Indonesia
  • Rahmad Dian Institut Teknologi Sawit Indonesia, Nort Sumatera, Indonesia
  • Ratu Mutiara Siregar Institut Teknologi Sawit Indonesia, Nort Sumatera, Indonesia
  • Raden Aris Sugianto Institut Teknologi Sawit Indonesia, Nort Sumatera, Indonesia

DOI:

https://doi.org/10.55537/jistr.v3i1.769

Keywords:

Face Detection, CCTV, IoT, Python

Abstract

Monitoring is a necessary part of Home surveillance that can be done through the internet network as a security measure. Many CCTV cameras on the market today continue to employ analog and conventional technology, specifically coaxial wire. As a result, extra expenditures for CCTV system wiring are required; besides being more expensive, the installation takes more handling, as the picture data cable and control signal cable cannot be merged. This project aims to develop a security system capable of detecting object movement in real-time utilizing a webcam camera attached to a raspberry pi. The findings of this study enable the development of a low-cost CCTV system that can be monitored remotely via the Internet of Things.

References

Q. Akariman, A. N. Jati, and A. Novianty, “Face recognition based on the Android device using LBP algorithm,” ICCEREC 2015 - Int. Conf. Control. Electron. Renew. Energy Commun., pp. 166–170, 2015.

S. Mallu, “PENDETEKSIAN GERAKAN MENGGUNAKAN INTERNET PROTOCOL CAMERA BERBASIS WEB,” J. Ilm. Teknol. Inf. Terap., vol. I, no. 3, pp. 9–14, 2015.

C. Liu, “The development trend of evaluating face-recognition technology,” Proc. - 2014 Int. Conf. Mechatronics Control. ICMC 2014, no. August 1994, pp. 1540–1544, 2015.

N. A. Mathew and A. Client, “IoT based Real Time Patient Monitoring and Analysis using Raspberry Pi 3,” 2017 Int. Conf. Energy, Commun. Data Anal. Soft Comput., pp. 2638–2640, 2017.

N. Patil, S. Ambatkar, and S. Kakde, “IoT Based Smart Surveillance Security System using Raspberry Pi,” pp. 344–348, 2017.

P. I. Y. Sheikh, H. C. Chavan, J. S. Vyawhare, S. B. Mahajan, and R. S. Raut, “Wi-Fi Surveillance Robot Using Raspberry Pi,” no. April, pp. 25–29, 2016.

M. Gutensohn, N. S. Session, and M. Gutensohn, “Developing a Natural User Interface and Facial Recognition System With OpenCV and the Microsoft Kinect,” pp. 1–6, 2018.

W. S. Pambudi, B. Maria, N. Simorangkir, J. T. Elektro, U. I. Batam, and D. Obyek, “Facetracker Menggunakan Metode Haar Like Feature,” J. Teknol. dan Inf., vol. 2, no. 2, pp. 142–154, 2012.

A. Mahmudi, “Deteksi Senjata Tajam Dengan Metode Haar Cascade Classifier Menggunakan Teknologi Sms Gateway,” Matics, vol. 1, no. 1, pp. 27–30, 2014.

L. W. Alexander, S. R. Sentinuwo, A. M. Sambul, T. Informatika, U. Sam, and R. Manado, “Implementasi Algoritma Pengenalan Wajah Untuk Mendeteksi Visual Hacking,” E-Journal Tek. Inform., vol. 11, no. 1, 2017.

S. Al-Aidid and D. Pamungkas, “Sistem Pengenalan Wajah dengan Algoritma Haar Cascade dan Local Binary Pattern Histogram,” J. Rekayasa Elektr., vol. 14, no. 1, pp. 62–67, 2018.

R. Puri and A. Gupta, “CONTOUR, SHAPE, AND COLOR DETECTION USING OPEN CV – PYTHON,” no. May, pp. 2–4, 2018.

P. Pasumarti and P. P. Sekhar, “Classroom Attendance Using Face Detection and Raspberry-Pi,” Int. Res. J. Eng. Technol., vol. 5, no. 1, pp. 167–171, 2018.

M. Phankokkruad, P. Jaturawat, and P. Pongmanawut, “A real-time face recognition for class participation enrollment system over WebRTC,” Eighth Int. Conf. Digit. Image Process. (ICDIP 2016), vol. 10033, no. August 2016, p. 100330V, 2016.

W. S. M. Sanjaya, D. Anggraeni, K. Zakaria, A. Juwardi, and M. Munawwaroh, “The design of face recognition and tracking for human-robot interaction,” Proc. - 2017 2nd Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. ICITISEE 2017, vol. 2018-Janua, no. c, pp. 315–320, 2018.

N. Sontakke, S. Kulthe, R. Gaikwad, and T. Ladkat, “Automatic Face Recognition Techniques using LBPH,” no. 3, pp. 1912–1916, 2018.

A. Rahim, N. Hossain, T. Wahid, and S. Azam, “Face Recognition using Local Binary Patterns (LBP),” Glob. J. Comput. Sience Technol. Graph. Vis., vol. 13, no. 4, pp. 469–481, 2013.

N. Stekas and D. Van Den Heuvel, “Face recognition using local binary patterns histograms (LBPH) on an FPGA-based system on chip (SoC),” Proc. - 2016 IEEE 30th Int. Parallel Distrib. Process. Symp. IPDPS 2016, pp. 300–304, 2016.

M. A. S. Pane, P. Ehkan, K. Saleh, and M. Irwanto, “A Mobile Surveillance Robot Over The Wifi Network Using Atmega 8,” vol. 20, no. 5, pp. 20–26, 2018.

N. Sakali and G. Nagendra, “Design and Implementation of Web Surveillance Robot for Video Monitoring and Motion Detection,” vol. 7, no. 2, pp. 4298–4302, 2017.

Huu-Quoc Nguyen, Ton Thi Kim Loan, Bui Dinh Mao, and Eui-Nam Huh, “Low cost real-time system monitoring using Raspberry Pi,” 2015 Seventh Int. Conf. Ubiquitous Futur. Networks, pp. 857–859, 2015.

K. Damodhar, B. Vanathi, and K. Shanmugam, “A Surveillance Robot For Real Time Monitoring And Capturing Controlled Using Android Mobile,” Middle-East J. Sci. Res., vol. 24, pp. 155–166, 2016.

S. P. Richard, S. S. R.U.A, and N. M. E.I, “Aplikasi Pengenalan Wajah untuk Sistem Absensi Kelas Berbasis Raspberry Pi,” J. Tek. Inform., vol. 15, no. 3, pp. 179–188, 2020.

Y. Zhou, D. Liu, and T. Huang, “Survey of face detection on low-quality images,” Proc. - 13th IEEE Int. Conf. Autom. Face Gesture Recognition, FG 2018, pp. 769–773, 2018.

R. S. Deepthi and S. Sankaraiah, “Implementation of mobile platform using Qt and OpenCV for image processing applications,” 2011 IEEE Conf. Open Syst. ICOS 2011, pp. 290–295, 2011.

Additional Files

Published

2024-01-31