Chintaparth iMounish Reddy, B V Naveen, Kanama Bharath Prakash Reddy, Bangarugari Venkata Sai, Prof.Priyadarshini R


Abstract: In this paper we are going to deal with the surveillance and security system using raspberry pi. We are using VGG face algorithm to process and extract the features in the image that help for facial recognition. We also make use of different type of sensors for creating a security system that will send alerts to the email.




Internet Of Things (IOT), Raspberry Pi-4, VGG Face, PIR Sensor

Full Text:



Indoor Intrusion Detection and Filtering System Using Raspberry Pi by Umi Najiah Ahmad Razimi Faculty of Information Sciences & Engineering Management and Science University Shah Alam, Selangor, Malaysia Shamla Devi Segar School of Graduates Studies Management and Science University Shah Alam, Selangor, Malaysia shamla_segar@gmail.com2020 16th IEEE International Colloquium on Signal Processing & its Applications (CSPA 2020), 28-29 Feb. 2020, Langkawi, Malaysia.

Security System Using Raspberry Pi Shakthi Murugan.K.H1 , V.Jacintha2 , S.Agnes Shifani3 1,2,3Assistant Professor, Department of Electronics and Communication Engineering, Jeppiaar Maamallan Engineering College, Chennai, India. 2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM).

Dhvani Shah ,Vinayak haradi,“IoT Based Biometrics Implementation on Raspberry Pi”, Elsevier Procedia Computer Science Volume 79, 2016, Pages 328–336.

Akash V. Bhatkule, Ulhas B. Shinde, Shrinivas R. Zanwar,” Home Based Security Control System using Raspberry Pi and GSM”,International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 4, Issue 9, September 2016, pp. 16259- 16264.

Khushbu H Mehta, Niti P Gupta, “Vision Based – Real Time Monitoring Security System for Smart Home” IOSR Journal of Electronics and Communication Engineering, Volume 9, Issue 5, Ver. V (Sep - Oct. 2014), PP 46-53.

S. Khedkar and G. M. Malwatkar, "Using raspberry Pi and GSM survey on home automation," 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, 2016, pp. 758-761.

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 International Conference on Ubiquitous and Future Networks, Sapporo, 2015, pp. 857-859.

V. Sandeep, K. L. Gopal, S. Naveen, A. Amudhan and L. S. Kumar, "Globally accessible machine automation using Raspberry pi based on Internet of Things," 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, 2015, pp. 1144-1147.

V. Menezes, V. Patchava and M. S. D. Gupta, "Surveillance and monitoring system using Raspberry Pi and SimpleCV," 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), Noida, 2015, pp. 1276-1278.

W. F. Abaya, J. Basa, M. Sy, A. C. Abad and E. P. Dadios, "Low cost smart security camera with night vision capability using Raspberry Pi and OpenCV," 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Palawan, 2014, pp. 1-6.

M. A. M. Isa, H. Hashim, J. l. A. Manan, S. F. S. Adnan and R. Mahmod, "RF simulator for cryptographic protocol," 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), Batu Ferringhi, 2014, pp. 518-523.

Razalli, H., Rahmat, R. W. O., Khalid, F., & Sulaiman, P. S. (2017). An Image-Based Children Age Range Verification and Classification Based on Facial Features Angle Distribution and Face Shape Elliptical Ratio. Advanced Science Letters, 23(5), 4026-4030.

Razalli, H., Rahmat, R. W. O., Khalid, F., & Sulaiman, P. S. (2016). Automated Facial Features Points Localization for Age Estimation Based on Ideal Frontal Symmetry and Proportion of the Face. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(10), 67-72.

Ashwinkumar.U.M and Dr. Anandakumar K.R, "Predicting Early Detection of cardiac and Diabetes symptoms using Data mining techniques", International conference on computer Design and Engineering, vol.49, 2012.

Hajamydeen, A. I., & Udzir, N. I. (2019). A Detailed Description on Unsupervised Heterogeneous Anomaly Based Intrusion Detection Framework. Scalable Computing: Practice and Experience, 20(1), 113-160.

Hajamydeen, A. I., & Udzir, N. I. (2016). A refined filter for UHAD to improve anomaly detection. Security and Communication Networks, 9(14), 2434-2447.



  • There are currently no refbacks.

Copyright (c) 2021 International Journal of Advanced Research in Computer Science