Abstract
Due to the frequent and widespread instances of burglary, effective and accurate intrusion detection with an alarm system is now necessary. In this study, the proposed system is used for secret and vital places that need a good monitoring system based on human motion detection. The system can protect museums that contain critical historical objects and libraries with old historical books. The security alert system is also essential to the protection of banks. The system uses a two-frame difference algorithm and a simple background modeling algorithm for motion detection with the help of AForge.NET. Whenever the system detects motion, it interprets the frames of motion to determine humans by using YOLO. The security alert system activates the alerts whenever it detects human motion, like sending an email to an authorized person, triggering the LED alert and sound alert, recording a video of that place, taking a motion picture, and saving it within the databases. The system has the advantages of low cost, a user-friendly interface, and high security. The system was thoroughly tested to detect human movement in unauthorized areas. The system successfully detected human activity indoors and outdoors and activated the alarms. Also, the system could detect human movement in all four directions and alert observers directly at the time of the incident. In low light or minimum illumination, the system can determine human motion. The evaluation results showed that the system's accuracy in detecting human movement was 95%, which shows that the system is able to protect vital and secret places and can successfully monitor areas.
Publisher
Sakarya University of Applied Sciences
Reference31 articles.
1. [1] A. Kandhalu, A. Rowe, R. Rajkumar, C. Huang, and C.-C. Yeh, “Real-time video surveillance over IEEE 802.11 mesh networks,” 2009 15th IEEE Real-Time and Embedded Technology and Applications Symposium, 2009.
2. [2] O. Elharrouss, N. Almaadeed, and S. Al-Maadeed, “A review of video surveillance systems,” Journal of Visual Communication and Image Representation, vol. 77, no. 1047–3203, 2021.
3. [3] N. O.C., A.-U. C.C., N. U.A., and E. C.H., “Motion Detector Security System for indoor geolocation,” International Journal of Engineering and Applied Sciences (IJEAS), vol. 5, no. 11, 2018.
4. [4] Nan Lu, Jihong Wang, Q.H Wu, Li Yang, An Improved Motion Detection Method for Real-Time Surveillance, International Journal Of Computer Science, vol.1, Issue 6, 2008.
5. [5] R. Huang, J. Pedoeem, and C. Chen, “Yolo-lite: a real- 19 time object detection algorithm optimized for non-gpu computers,” in 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018, pp. 2503–2510.