Towards a Crowd Analytic Framework For Crowd Management in Majid-al-Haram

TL;DR

This paper presents a computer vision framework for analyzing crowd movement and congestion in the holy cities of Makkah and Madina during Hajj, aiding crowd management with automated measurements.

Contribution

It introduces a novel computer vision-based system tailored for high-density crowd analysis in religious sites, addressing unique spatial and temporal challenges.

Findings

Effective crowd density estimation demonstrated

Identification of dominant movement patterns achieved

Congestion detection and localization validated

Abstract

The scared cities of Makkah Al Mukarramah and Madina Al Munawarah host millions of pilgrims every year. During Hajj, the movement of large number of people has a unique spatial and temporal constraints, which makes Hajj one of toughest challenges for crowd management. In this paper, we propose a computer vision based framework that automatically analyses video sequence and computes important measurements which include estimation of crowd density, identification of dominant patterns, detection and localization of congestion. In addition, we analyze helpful statistics of the crowd like speed, and direction, that could provide support to crowd management personnel. The framework presented in this paper indicate that new advances in computer vision and machine learning can be leveraged effectively for challenging and high density crowd management applications. However, significant customization of existing approaches is required to apply them to the challenging crowd management situations in Masjid Al Haram. Our results paint a promising picture for deployment of computer vision technologies to assist in quantitative measurement of crowd size, density and congestion.