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The scared cities of Makkah Al Mukarramah and Madina Al Munawarah hostmillions of pilgrims every year. During Hajj, the movement of large number ofpeople has a unique spatial and temporal constraints, which makes Hajj one oftoughest challenges for crowd management. In this paper, we propose a computervision based framework that automatically analyses video sequence and computesimportant 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, anddirection, that could provide support to crowd management personnel. Theframework presented in this paper indicate that new advances in computer visionand machine learning can be leveraged effectively for challenging and highdensity crowd management applications. However, significant customization ofexisting approaches is required to apply them to the challenging crowdmanagement situations in Masjid Al Haram. Our results paint a promising picturefor deployment of computer vision technologies to assist in quantitativemeasurement of crowd size, density and congestion.

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