Intrusion Eye Detector

Year : 2025 | Volume : 03 | Issue : 01 | Page : 28 32
    By

    Neha Prajapati,

  • Sushma Dwivedi,

  • Arshita Mishra,

  • Manvi Mishra,

  • Singh Janvi Kumari,

  1. Assistant Professor, Department of CSE-AI & AIML, Bansal Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India
  2. Student, Department of CSE-AIML, Bansal Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India
  3. Student, Department of CSE-AIML, Bansal Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India
  4. Student, Department of CSE-AIML, Bansal Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India
  5. Student, Department of CSE-AIML, Bansal Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India

Abstract

Uncertainty about security risks and illegal access are becoming more prevalent in a variety of settings, such as private homes, business buildings, and critical government buildings. Conventional security systems, which may not offer real-time warnings or prompt reactions, frequently rely on passive monitoring, including CCTV cameras and motion sensors. Artificial intelligence (AI) and computer vision-based intelligent systems are becoming more and more popular as a means of improving security and monitoring. These cutting-edge technologies enhance safety and lower possible risks by actively detecting, monitoring, and reacting to security breaches in real time. To address these challenges, this research paper proposes an advanced “Intrusion Eye Detector” system that integrates computer vision, real-time surveillance, and remote alert capabilities for enhanced security. The system utilizes a webcam to detect intruders and employs human pose detection to identify unauthorized individuals with greater accuracy. Once an intrusion is detected, the system sends an image-based SMS notification to the system owner, allowing for quick response and immediate action. Additionally, it features voice alerts to warn individuals on-site and deter potential threats. To ensure crucial evidence is captured, the system includes functionalities for taking screenshots and recording videos, which can be stored for future reference. Furthermore, cloud storage integration allows for secure image backup, and remote monitoring is enabled through an SMS notification containing a URL link to the uploaded image. This combination of real-time detection, automated alerts, and cloud-based evidence storage makes the Intrusion Eye Detector a highly efficient and reliable security solution.

Keywords: Video processing, facial recognition, pose estimation, Twilio Api for alerts, Intruder detector

[This article belongs to International Journal of Optical Innovations & Research ]

How to cite this article:
Neha Prajapati, Sushma Dwivedi, Arshita Mishra, Manvi Mishra, Singh Janvi Kumari. Intrusion Eye Detector. International Journal of Optical Innovations & Research. 2025; 03(01):28-32.
How to cite this URL:
Neha Prajapati, Sushma Dwivedi, Arshita Mishra, Manvi Mishra, Singh Janvi Kumari. Intrusion Eye Detector. International Journal of Optical Innovations & Research. 2025; 03(01):28-32. Available from: https://journals.stmjournals.com/ijoir/article=2025/view=206767


References

1. Bhattacharya M, Pujari S, Anand A, Kumar N, Jha SK, Raj A, Hossain SM. Intruder Detection System using Posture Recognition and Machine Learning. Int J Comput Appl. 2021; 183(19): 17–23.
2. Setiawan W. Sistem deteksi manusia berbasis kamera esp32cam dan opencv python dengan pemberitahuan telegram. Doctoral Dissertation. Universitas Teknologi Digital Indonesia; 2025.

3. Choi B. Python Network Automation Virtual Lab 2: Sendmail Email Notification and Twilio SMS Notification on Docker. In: Introduction to Python Network Automation Volume II: Stepping up: Beyond the Essentials for Success. Berkeley, CA: Apress; 2024 Jul 10; 405–471.
4. Mishra N, Rodge A, Deo M, Agalwe P, Bansode N. Road Surveillance using Real-Time Detection and Alert System. In 2024 IEEE 4th International Conference on Pervasive Computing and Social Networking (ICPCSN). 2024 May 3; 797–802.
5. Boyko N. Use of a cloud storage for implementation informational processes. ECONTECHMOD:an International Quarterly Journal on Economics of Technology and Modelling Processes. 2017; 6(2): 3–8.
6. Khandelwal Y, Goyal R, Negi P. User Interface based Text-To-Speech Synthesizer. In 2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC). 2024 Jan 8; 1–8.
7. Abhinay D, Chaitanya K, Ram PS. Intruder Detection and Tracking Using Computer Vision and IoT. In: Advances in Signal Processing and Communication Engineering: Select Proceedings of ICASPACE 2021. Singapore: Springer Nature Singapore; 2022 Dec 2; 499–512.
8. Lundin E, Jonsson E. Anomaly-based intrusion detection: privacy concerns and other problems. Comput Netw. 2000 Oct 1; 34(4): 623–40.
9. Liu X, Jiang R, Wang H, Ge SS. Filter-based secure dynamic pose estimation for autonomous vehicles. IEEE Sens J. 2019 Apr 9; 19(15): 6298–308.
10. Paramasivam A, Bargavi S, Priyadharshini R, Subhiksha M, Vijayalakshmi S, NM MB. Internet of things-based fall prediction and alerting device. In 2022 IEEE International Conference on Communication, Computing and Internet of Things (IC3IoT). 2022 Mar 10; 01–05.
11. Zhu Z, Fujimura K, Ji Q. Real-time eye detection and tracking under various light conditions. In:Proceedings of the 2002 Symposium on Eye Tracking Research & Applications. New York (NY): Association for Computing Machinery; 2002. p. 139–44. DOI: 10.1145/507072.507100.

Regular Issue Subscription Original Research
Volume 03
Issue 01
Received 03/03/2025
Accepted 25/03/2025
Published 05/04/2025
Publication Time 33 Days
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