Monitoring Systems for Counting People Based on Wireless Multimedia Sensor Network
-
Kalvein Rantelobo(1*)
Universitas Nusa Cendana
-
Agusthinus Sampeallo(2)
Universitas Nusa Cendana
-
J. F. Mandala(3)
Universitas Nusa Cendana
-
H. F. J. Lami(4)
Universitas Nusa Cendana
-
B. Bernandus(5)
Universitas Nusa Cendana
-
P. H. Rantelinggi(6)
Universitas Papua
-
N. P. Sastra(7)
Universitas Udayana
(*) Corresponding Author
Abstract
The visual sensor used in the Wireless Sensor Multimedia Networks (WSMN) in this work aims to monitor and calculate the number of people passing through a room. The contribution of this paper is the use of Raspberry Pi 3 devices that are connected to the internet using Internet-of-Thing (IoT) technology. The proposed scheme can be implemented in the actual environment. From the test results, the system has distinguished people entering and leaving the room by doing image processing using background subtraction, morphological transformation method, and calculating the contour area of the image. The results of image processing can calculate the number of people in the room, and the system can send it to the web server. Subsequently, this paper discussed the energy consumption used by the WSMN and explained test parameters.
Downloads
References
S. Birtane, O. K. Sahingoz, and H. Korkmaz, "Vibrational genetic algorithm-based deployment of wireless sensor networks with heterogeneous nodes in irregularly shaped areas," IEEE Access, 2024, doi: https://doi.org/10.1109/ACCESS.2024.3395421.
S. Lata, S. Mehfuz, and S. Urooj, "Secure and reliable WSN for Internet of Things: Challenges and enabling technologies," IEEE Access, vol. 9, pp. 161103-161128, 2021, doi: https://doi.org/10.1109/ACCESS.2021.3131367.
Z. Nurlan, T. Zhukabayeva, M. Othman, A. Adamova, and N. Zhakiyev, "Wireless Sensor Network as a Mesh: Vision and Challenges," IEEE Access, vol. 10, pp. 46-67, 2022, doi: https://doi.org/10.1109/ACCESS.2021.3137341.
D. T. Nguyen, W. Li, and P. O. Ogunbona, "Human detection from images and videos: A survey," Pattern Recognition, vol. 51, pp. 148-175, 2016, doi: https://doi.org/10.1016/j.patcog.2015.08.027.
N. P. Sastra and G. Hendrantoro, "Energy Efficiency of Image Transmission in Embedded Linux based Wireless Visual Sensor Network," Journal of Communications Software and Systems, vol. 11, no. 3, pp. 146-154, 2015, doi: http://dx.doi.org/10.24138/jcomss.v11i3.103.
N. P. Sastra, W. Wirawan, and G. Hendrantoro, "Virtual view image over wireless visual sensor network," TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 9, no. 3, pp. 489-496, 2011, doi: http://doi.org/10.12928/telkomnika.v9i3.740.
D. M. Wiharta and G. Hendrantoro, "On the Accuracy of Particle Filter-Based Object Tracking," International Journal of Multimedia and Ubiquitous Engineering, vol. 10, no. 11, pp. 265-276, 2015, doi: https://doi.org/10.14257/ijmue.2015.10.11.25.
T. Teixeira and A. Savvides, "Lightweight people counting and localizing for easily deployable indoors wsns," IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 4, pp. 493-502, 2008, doi: https://doi.org/10.1109/JSTSP.2008.2001426.
B. Hemangi and K. Nikhita, "People counting system using raspberry pi with opencv," International Journal for Research in Engineering Application & Management (IJREAM), ISSN, pp. 2494-9150, 2016. [Online]. Available: https://www.ijream.org/papers/IJREAMV02I01894.pdf.
D. I. S. Saputra, "Rancang Bangun Alat Penghitung Jumlah Pengunjung di Toko Adhelina Berbasis Mikrokontroler Atmega 16," Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 4, no. 1, pp. 16-21, 2015. [Online]. Available: https://media.neliti.com/media/publications/265926-rancang-bangun-alat-penghitung-jumlah-pe-38a1e6b1.pdf.
R. Kusumanto, W. S. Pambudi, and A. N. Tompunu, "Aplikasi Sensor Vision untuk Deteksi MultiFace dan Menghitung Jumlah Orang," Semantik 2012, pp. 1-8, 2012. [Online]. Available: https://publikasi.dinus.ac.id/index.php/semantik/article/view/45.
R. Pi, "Raspberry pi-teach, learn, and make with raspberry pi," línea]. Available: https://www.raspberrypi.org/[Último acceso: 20 Agosto 2016], 2015.
python. [Online]. Available: https://www.python.org/downloads/
Lbrary opencv. Accessed: 04-Sep-2018. [Online]. Available: https://opencv.org
IoT Analytics - ThingSpeak Internet of Things. [Online]. Available: https://thingspeak.mathworks.com/
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This work is licensed under CC BY-SA 4.0
