Crowd Size Estimation and Detecting Social Distancing using Raspberry Pi and OpenCV

• Autorzy: Milton Davidson Kamala Dhas , Velraj Arun Ra

Identyfikatory

DOI

10.24425/ijet.2023.144326

• Języki publikacji: EN

Abstrakty

EN

In this covid19 pandemic the number of people gathering at public places and festivals are restricted and maintaining social distancing is practiced throughout the world. Managing the crowd is always a challenging task. It requires monitoring technology. In this paper, we develop a device that detects and provide human count and detects people who are not maintaining social distancing. The work depicted above was finished using a Raspberry Pi 3 board with OpenCV-Python. This method can effectively manage crowds.

Słowa kluczowe

EN

Haar features; human detection; distance checking; raspberry pi; opencv

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2023
• Tom: Vol. 69, No. 1
• Strony: 19--24
• Opis fizyczny: Bibliogr. 12 poz., fot., rys., tab.

Twórcy

autor

Milton Davidson Kamala Dhas

• Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamil Nadu, India

autor

Velraj Arun Ra

• Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamil Nadu, India

Bibliografia

• [1] Abbas, S. Syed Ameer, M. Anitha, and X. Vinitha Jaini. "Realization of multiple human head detection and direction movement using Raspberry Pi." In 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 1160-1164. IEEE, 2017. https://doi.org/10.1109/WiSPNET.2017.8299946.
• [2] Rucha Visal, Atharva Theurkar, Bhairavi Shukla , “Monitoring Social Distancing for Covid-19 Using OpenCV and Deep Learning”, International Research Journal of Engineering and Technology (IRJET) Volume: 07 Issue: 06, p-ISSN: 2395-0072, June 2020.
• [3] Ms. Subashree D, Shrushti Rohidas Mhaske, Sonal Rajesh Yeshwantrao, Ayush Kumar, “Real Time Crowd Counting using OpenCV”, International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181, Vol. 10 Issue 05, May 2021.
• [4] Badhan Hemangi, K. Nikhita, “People counting system using raspberry pi with opencv,” International Journal for Research in Engineering Application & Management (IJREAM), ISSN : 2494-9150 Vol-02, Issue 01, APR 2016. https://doi.org/10.1109/ELINFOCOM.2016.7563020.
• [5] Kanchan Mangrule, H. T. Ingale, Vijay D. Chaudhari, Dr. A. J. Patil, “Literature Survey of Iot Capabled Crowd Analysis Using Raspberry Pi-3,” International Journal of Innovations in Engineering and Science, Vol 4, No.10, 2019.
• [6] Md Israfil Ansari, Shim Jaechang, “People Counting System using Raspberry Pi”, Journal of Multimedia Information System VOL. 4, NO. 4, pp. 239-242, December 2017.
• [7] A Jaysri Thangam, Padmini Thupalli Siva, B.Yogameena “Crowd Video Count In Low Resolution Surveillance Head Detector and Color based using Segmentation for Disaster Management”, IEEE ICCSP 2015 conference.
• [8] Songyan Ma, Tiancang Du, “Improved Adaboost Face Detection,” International conference on measuring technology and mechatronics automation, 2010. https://doi.org/10.1109/ICMTMA.2010.184.
• [9] Min Li, Zhaoxiang Zhang, Kaiqi Huang and Tieniu Tan, “Estimating the Number of People in Crowded Scenes by MID Based Foreground Segmentation and Head-shoulder Detection,” National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, IEEE, 2008 https://doi.org/10.1109/ICPR.2008.4761705.
• [10] Michael D. Breitenstein, Fabian Reichlin, Bastian Leibe, Esther Koller-Meier, and Luc Van Gool, “Online Multiperson Tracking-by-Detection from a Single uncalibrated Camera”, IEEE transactions on pattern analysis and machine intelligence, vol. 33, no. 9, September 2011. https://doi.org/10.1109/TPAMI.2010.232.
• [11] Tao Zhao, Ram Nevatia, “Tracking Multiple Humans in Complex Situations”, IEEE transactions on pattern analysis and machine intelligence, vol. 26, no. 9, September 2004. https://doi.org/10.1109/TPAMI.2004.73.
• [12] G V Shalini , M Kavitha Margret , M J Sufiya Niraimathi , S Subashree, ”Social Distancing Analyzer Using Computer Vision and Deep Learning”, Journal of Physics: Conference Series, 2021. https://doi.org/10.1088/1742-6596/1916/1/012039.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).