Artificial Intelligence Based Intruder Detection Home Security System

Authors

  • M. Jahir Pasha Associate Professor, Department of Computer Science and Engineering, G.Pulllaiah College of Engineering and Technology, Kurnool, India
  • B. Maruthi Shankar Associate Professor-ECE, Sri Krishna College of Engineering and Technology, Coimbatore, India
  • S. A. Sivakumar Associate Professor-ECE, Dr.N.G.P. Institute of Technology, Coimbatore, India
  • S. Aswin UG Scholar-ECE, Sri Krishna College of Engineering and Technology, Coimbatore, India
  • A. Bragadeesh UG Scholar-ECE, Sri Krishna College of Engineering and Technology, Coimbatore, India
  • S. Deepak UG Scholar-ECE, Sri Krishna College of Engineering and Technology, Coimbatore, India

Keywords:

Artificial Intelligence, face detection, Grassmann's algorithm, Intruders and burglars

Abstract

An effective face detection and identification algorithm is required to design a highly effective intruder detecting surveillance system. When a person is captured on camera, CNN, an artificial intelligence programme that detects objects, and Grassmann's algorithm are utilised to determine whether or not the individual is an invader. The research's objective is to identify the quickest method for homeowners to be notified if a burglar or intruder breaks into their house utilising a proactive surveillance system. This device's programming was based on several recognition algorithms and a framework for evaluating factors that might distinguish between intruders and burglars. Developmental research was employed in the design to address the research topic. The outcome demonstrates that the system is capable of spotting and identifying burglars and can inform the homeowners in advance via email and emergency alarms. The technology can identify intruders, alert the family members in advance, and activate the home's alarm system, it is determined

Downloads

Download data is not yet available.

References

Agustina J. R. and Clavell G (2019) "The Impact of CCTV on Fundamental Rights and Crime Prevention Strategies: The Case of the Catalan Control Commission of Video Surveillance Devices," Computer Law and Security Review, pp. 168-174, doi:10.1016/j.clsr.2020.01.006.

Banuchitra S and Kungumaraj K (2019) “A comprehensive survey of content-based image retrieval techniques,” International Journal of Engineering and Computer Science, vol.5, no.8, pp.17577-17584, doi:10.18535/ijecs/v5i8.26.

Çarıkçı M and Özen F (2021) “A face recognition system based on eigenfaces method, ”Procedia Technology, vol. 1, pp. 118-123, doi:10.1016/j.protcy.2012.02.023

Chang X , Nie F ,Wang S ,Yang Y, Zhou X, and C. Zhang (2019) “Compound rank-k projections for bilinear analysis,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 7, pp. 1502–1513.

Elfasakhany, Hernández J, García J. C, Reyes M, and Martell F (2020)“Design and development of a house-mobile security system,” Engineering, vol.03,no.12,pp.1213-1224,doi:10.4236/eng.2011.312151.

Faux Fand Luthon F (2019) “Theory of evidence for face detection and tracking, ”International Journal of Approximate Reasoning, vol. 53, no. 5, pp. 728-746,doi:10.1016/j.ijar.2019.02.002.

Li S (2020) “The application of face recognition based on opencv,” Advanced Materials Research, vol.403-408, pp.2350-

Li W ,Zhao R ,Xiao T, and Wang X (2019) “Deepreid: Deep filter pairing neural network for person re-identification,” in Proc. CVPR, pp. 152–159.

Liao S, Hu Y, Zhu X, and Li S Z (2018) “Person re-identification by local maximal occurrence representation and metric learning,” in Proc. CVPR, pp. 2197–2206.

Ni JandChellappa R (2022)“Evaluationofstate-of-the-artalgorithmsforremoteface recognition,” 2010 IEEE International Conference on Image Processing,doi:10.1109/icip.2010.5652608.

Reza Khan S and Sultana Dristy F (2020) “Android based security and HomeAutomationSystem,”TheInternationalJournalofAmbientSystemsandApplications, vol. 3, no. 1, pp. 15-24, doi: 10.5121/ijasa.2014.3102. ISSN:2502-4752IndonesianJElecEng&CompSci,Vol.24,No.2,November2021:843-852852

Richey R C and Klein J D, (2019) “Developmental research methods: Creating knowledge from instructional design and development practice,” Journal of Computing in Higher Education, vol.16, no.2,pp.23-38,doi:10.1007/bf02961473.

Viola P and Jones M. J. (2020) “Robust real-time face detection,” International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154,doi:10.1023/b:visi.0000013087.49260.fb.

Wang Y.Q (2019) “An analysis of the viola-jones face detection algorithm” Image Processing On Line, vol.4, pp.128-148,doi:10.5201/ipol.2014.104.

Xia R, Pan Y, Lai H, Liu C, and Yan S (2018) “Supervised hashing for image retrieval via image representation learning.” in Proc. AAAI , pp. 2156–2162.

Yang M, Li F, Zhang L, and Zhang Z (2018) “Deep learning algorithm withvisual impression,” Information Processing Letters, vol. 136, pp. 1-4, doi:10.1016/j.ipl.2018.03.004.

Yang Y ,Nie F, Xu D ,Luo J ,Zhuang Y, and Pan Y (2020) “A multimedia retrieval framework based on semi-supervised ranking and relevance feedback,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 4, pp. 723–742.

Yang Y, Ma Z ,Hauptmann A. G, and Sebe N (2021) “Feature selection for multimedia analysis by sharing information among multiple tasks,” IEEE Transactions on Multimedia, vol. 15, no. 3, pp. 661–669.

Zheng L, Bie Z, Sun Y, Wang J, Wang S, Su C, and Tian Q(2022) “Mars: A video benchmark for large-scale person re-identification,” in Proc. ECCV, pp. 868–884.

Zheng L, Shen L, Tian L, Wang S, Wang J, and Tian Q (2021) “Scalable person re-identification: A benchmark,” in Proc. ICCV, pp. 1116– 1124.

Zheng L, Wang S, Tian L, He F, Liu Z, and Tian Q(2019) “Query-adaptive late fusion for image search and person re-identification,” in Proc. CVPR, pp. 1741–1750.

Russo, L., Kamińska, K., Christensen, M., Martínez, L., & Costa, A. Machine Learning for Real-Time Decision Support in Engineering Operations. Kuwait Journal of Machine Learning, 1(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/117

Anand, R., Ahamad, S., Veeraiah, V., Janardan, S.K., Dhabliya, D., Sindhwani, N., Gupta, A. Optimizing 6G wireless network security for effective communication (2023) Innovative Smart Materials Used in Wireless Communication Technology, pp. 1-20.

Downloads

Published

24.11.2023

How to Cite

Pasha, M. J. ., Shankar, B. M. ., Sivakumar, S. A. ., Aswin, S. ., Bragadeesh, A. ., & Deepak, S. . (2023). Artificial Intelligence Based Intruder Detection Home Security System. International Journal of Intelligent Systems and Applications in Engineering, 12(5s), 295–300. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3888

Issue

Vol. 12 No. 5s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.

Most read articles by the same author(s)