A New Surveillance and Security Alert System Based on Real-Time Motion Detection

Öz

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.

Anahtar Kelimeler

• Human motion detection
• security alert system
• YOLO
• camera assisted security
• Surveillance system

GERÇEK ZAMANLI HAREKET ALGILAMAYA DAYALI BİR GÖZETİM VE GÜVENLİK UYARI SİSTEMİ

Öz

Hırsızlık olaylarının sık ve yaygın olması nedeniyle, artık bir alarm sistemiyle etkin ve doğru izinsiz giriş tespiti gereklidir. Bu çalışmada önerilen sistem, insan hareket algılamaya dayalı iyi bir izleme sistemine ihtiyaç duyan gizli ve hayati yerler için kullanılmıştır. Sistem, kritik tarihi objeler içeren müzeleri ve eski tarihi kitapların bulunduğu kütüphaneleri koruyabilir. Güvenlik uyarı sistemi, bankaların korunması için de gereklidir. Sistem, AForge.NET yardımıyla hareket algılama için iki çerçeveli bir fark algoritması ve basit bir arka plan modelleme algoritması kullanır. Sistem hareket algıladığında, YOLO kullanarak insanları belirlemek için hareket çerçevelerini yorumlar. Güvenlik uyarı sistemi, yetkili bir kişiye e-posta gönderme, LED uyarı ve sesli uyarıyı tetikleme, o yerin videosunu kaydetme, hareketli görüntü çekme ve veritabanlarına kaydetme gibi insan hareketlerini algıladığında uyarıları etkinleştirir. Sistem, düşük maliyet, kullanıcı dostu arayüz ve yüksek güvenlik avantajlarına sahiptir. Sistem, yetkisiz alanlarda insan hareketini tespit etmek için kapsamlı bir şekilde test edildi. Sistem, iç ve dış mekanlarda insan faaliyetlerini başarıyla tespit etti ve alarmları etkinleştirdi. Ayrıca sistem, dört yöndeki insan hareketini algılayabilir ve gözlemcileri olay anında doğrudan uyarabilir. Düşük ışıkta veya minimum aydınlatmada, sistem insan hareketini belirleyebilir. Değerlendirme sonuçları, sistemin insan hareketini algılamadaki doğruluğunun %95 olduğunu, bu da sistemin hayati ve gizli yerleri koruyabildiğini ve alanları başarılı bir şekilde izleyebildiğini gösteriyor.

Anahtar Kelimeler

• İnsan hareket algılama
• güvenlik uyarı sistemi
• YOLO
• kamera destekli güvenlik

Kaynakça

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.
6. [6] Mr. V N M Sarma Y, Miss. Lakshmi Durga T, Miss. Sireesha R, Mr. Anil Kumar P, Mr. P T S Kiran G, Mr. Mahaboob Ali S K, 2016, Saving Data Base Memory in Surveillance Camera by Real Time Motion Detection, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) NCACSPV – 2016 (Volume 4 – Issue 18).
7. [7] Li Fang, Zhang Meng, Claire Chen, and Qian Hui, "Smart Motion Detection Surveillance System," in 2009 International Conference on Education Technology and Computer, Singapore, 2009, pp. 171-175.
8. [8] Jadhav, Bhakti, Parth Deshmukh, and Nikhil Bajaj. "MOTION ACTIVATED SECURITY SYSTEM." (2019).

9. [9] A. Khan and M. Iqbal, A motion detection-based surveillance system (MDSS), The First International Conference on Conference: Informatics and Computational Intelligence (ICI), 2011.
10. [10] A. Sinha, P. Punjabi, S. More, J. Desai, and C. Chouhan, “Virtual Events using Laser Pointer,” International Research Journal of Engineering and Technology (IRJET), vol. 04, no. 04, 2017.
11. [11] K. Sehairi, F. Chouireb, and J. Meunier, “Comparative study of motion detection methods for video surveillance systems,” Journal of Electronic Imaging, vol. 26, no. 2, p. 023025, Apr. 2017, DOI: 10.1117/1.jei.26.2.023025.
12. [12] O. Elharrouss, D. Moujahid, S. Elkah, and H. Tairi, “Moving object detection using a background modeling based on entropy theory and quad-tree decomposition,” Journal of Electronic Imaging, vol. 25, no. 6, p. 061615, Nov. 2016, DOI: 10.1117/1.jei.25.6.061615.
13. [13] “Motion Detection,” aforge.net. http://www.aforgenet.com/framework/features/motion_detection_2.0.html (accessed Sep. 09, 2022).
14. [14] C. Zhan, X. Duan, S. Xu, Z. Song, and M. Luo, “An improved moving object detection algorithm based on frame difference and edge detection,” in Image and Graphics, 2007. ICIG 2007. Fourth International Conference on. IEEE, 2007, pp. 519–523.
15. [15] N. Singla, “Motion detection based on frame difference method,” International Journal of Information & Computation Technology, vol. 4, no. 15, 2014, pp. 1559-1565.
16. [16] H. Hassanpour, M. Sedighi, and A. R. Manashty, “Video Frame’s Background Modeling: Reviewing the Techniques,” Journal of Signal and Information Processing, vol. 02, no. 02, pp. 72–78, 2011, doi: 10.4236/jsip.2011.22010.
17. [17] D.S. Patil, R.B. Khanderay, and T. Padvi, “Survey on Moving Body Detection in Video Surveillance System,” International Journal of Engineering and Techniques, Vol. 1, No. 3, pp. 123-128, 2015.
18. [18] ÇAKIR, Hüseyin, and Habibe Kübra BABACAN. "Hareketi Algılayan Kamera Destekli Güvenlik Programı" INTERNATIONAL JOURNAL OF INFORMATICS TECHNOLOGIES 4.2 (2011).
19. [19] B. Y. Suprapto, A. Wahyudin, H. Hikmarika, and S. Dwijayanti, “The Detection System of Helipad for Unmanned Aerial Vehicle Landing Using YOLO Algorithm,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 7, no. 2, p. 193, Jun. 2021, doi: 10.26555/jiteki.v7i2.20684.
20. [20] C. Liu, Y. Tao, J. Liang, K. Li, and Y. Chen, “Object detection based on YOLO network,” in 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, 2018, pp. 799-803, DOI: 10.1109/ITOEC.2018.8740604.
21. [21] Zhang, S.; Cao, J.; Zhang, Q.; Zhang, Q.; Zhang, Y.; Wang, Y. An FPGA-Based Reconfigurable CNN Accelerator for YOLO. In Proceedings of the 2020 IEEE 3rd International Conference on Electronics Technology (ICET), Chengdu, China, 8–12 May 2020; pp. 74–78.
22. [22] Y. J. Wai, Z. bin Mohd Yussof, S. I. bin Salim, and L. K. Chuan, “Fixed Point Implementation of Tiny-Yolo-v2 using OpenCL on FPGA,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 10, 2018.
23. [23] I. V. Kiselev, "Comparative analysis of libraries for computer vision OpenCV and AForge. NET for use in gesture recognition system", In Journal of Physics: Conference Series, vol. 1661, no. 1, pp. 012048, 2020, November.
24. [24] Sureswaran, R., Al Bazar, H., Abouabdalla, O., Manasrah, A.M., El-Taj, H.: Active e-mail system SMTP protocol monitoring algorithm. In: 2nd IEEE International Conference on Broadband Network and Multimedia Technology, IC-BNMT 2009, October 2009.
25. [25] Leo Louis, “Working Principle of Arduino and Using It as a tool for study and research,” International Journal of Control, Automation, Communication and Systems (IJCACS), Vol.1, No.2, April 2016.
26. [26] Galeriu, C., Edwards, S., and Esper, G., “An Arduino investigation of simple harmonic motion,” The Physics Teacher, 52(3), 157-159 (March 2014).
27. [27] Badamasi, Y.A., "The working principle of an Arduino," in Electronics, Computer and Computation (ICECCO), 2014 11th International Conference on , vol., no., pp.1-4, Sept. 29 2014-Oct. 1 2014 doi: 10.1109/ICECCO.2014.6997578
28. [28] Widiyasono, Nur, Alam Rahmatulloh, and Helmi Firmansah. "Automatic Email Alert on the Internet of Things-based Smart Motion Detection System." In ICONISTECH-1 2019: Selected Papers from the 1st International Conference on Islam, Science and Technology, ICONISTECH-1 2019, 11-12 July 2019, Bandung, Indonesia, p. 133. European Alliance for Innovation, 2020.
29. [29] N. Surantha, W.R. Wicaksono, Design of smart home security system using object recognition and PIR sensor, Procedia Comput. Sci. 135 (2018) 465e472.
30. [30] M. Hasnain, M. F. Pasha, I. Ghani, M. Imran, M. Y. Alzahrani, and R. Budiarto, ‘‘Evaluating trust prediction and confusion matrix measures for web services ranking,’’ IEEE Access, vol. 8, pp. 90847–90861, 2020.
31. [31] S. Visa, B. Ramsay, A. L. Ralescu, and E. Van Der Knaap, ‘‘Confusion matrix-based feature selection,’’ in Proc. Midwest Artif. Intel. Cognit. Scienc. Conf., vol. 710, 2011, pp. 120–127.