Open Access

ARTICLE

Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

by None Durr-e-Nayab¹, Ali Mustafa Qamar^2,*, Rehan Ullah Khan³, Waleed Albattah³, Khalil Khan⁴, Shabana Habib³, Muhammad Islam⁵

1 Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar, Pakistan
2 Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia
3 Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
4 Department of Information Technology and Computer Science, Pak-Austria Fachhochschule, Institute of Applied Sciences and Technology, Haripur, Pakistan
5 Department of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Al-Qassim, Saudi Arabia

* Corresponding Author: Ali Mustafa Qamar. Email:

(This article belongs to the Special Issue: Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)

Computers, Materials & Continua 2022, 71(3), 5581-5601. https://doi.org/10.32604/cmc.2022.022153

Received 29 July 2021; Accepted 02 September 2021; Issue published 14 January 2022

Abstract

The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks. Video surveillance and crowd management using video analysis techniques have significantly impacted today's research, and numerous applications have been developed in this domain. This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis. Managing the Kaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic. The Umrah videos are analyzed, and a system is devised that can track and monitor the crowd flow in Kaaba. The crowd in these videos is sparse due to the pandemic, and we have developed a technique to track the maximum crowd flow and detect any object (person) moving in the direction unlikely of the major flow. We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow. Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity to maintain a smooth crowd flow in Kaaba during the pandemic.

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Keywords

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