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Utilization of Deep Learning-Based Crowd Analysis for Safety Surveillance and Spread Control of COVID-19 Pandemic

Osama S. Faragallah1,*, Sultan S. Alshamrani1, Heba M. El-Hoseny2, Mohammed A. AlZain1, Emad Sami Jaha3, Hala S. El-Sayed4

1 Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. 11099, Taif 21944, Saudi Arabia
2 Department of Electronics and Electrical Communication Engineering, Al-Obour High Institute for Engineering and Technology, Obour 3036, Egypt
3 Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
4 Department of Electrical Engineering, Faculty of Engineering, Menoufia University, Shebin El-Kom, 32511, Egypt

* Corresponding Author: Osama S. Faragallah. Email: email

Intelligent Automation & Soft Computing 2022, 31(3), 1483-1497. https://doi.org/10.32604/iasc.2022.020330

Abstract

Crowd monitoring analysis has become an important challenge in academic researches ranging from surveillance equipment to people behavior using different algorithms. The crowd counting schemes can be typically processed in two steps, the images ground truth density maps which are obtained from ground truth density map creation and the deep learning to estimate density map from density map estimation. The pandemic of COVID-19 has changed our world in few months and has put the normal human life to a halt due to its rapid spread and high danger. Therefore, several precautions are taken into account during COVID-19 to slowdown the new cases rate like maintaining social distancing via crowd estimation. This manuscript presents an efficient detection model for the crowd counting and social distancing between visitors in the two holy mosques, Al Masjid Al Haram in Mecca and the Prophet’s Mosque in Medina. Also, the manuscript develops a secure crowd monitoring structure based on the convolutional neural network (CNN) model using real datasets of images for the two holy mosques. The proposed framework is divided into two procedures, crowd counting and crowd recognition using datasets of different densities. To confirm the effectiveness of the proposed model, some metrics are employed for crowd analysis, which proves the monitoring efficiency of the proposed model with superior accuracy. Also, it is very adaptive to different crowd density levels and robust to scale changes in several places.

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Cite This Article

O. S. Faragallah, S. S. Alshamrani, H. M. El-Hoseny, M. A. AlZain, E. Sami Jaha et al., "Utilization of deep learning-based crowd analysis for safety surveillance and spread control of covid-19 pandemic," Intelligent Automation & Soft Computing, vol. 31, no.3, pp. 1483–1497, 2022.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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