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Abstract

Background & Objectives: The Hajj and minor Muslim pilgrimage of Umrah, present some of the most densely crowded human environments in the world, and thus offer an excellent testbed for the study of dense crowd dynamics. Accurate characterisation of such crowds is crucial to improve simulations that are ubiquitously applied to crowded environments such as train stations, and which require a high degree of detailed parameterisation. Accurate measurements of key crowd parameters can also help to develop better strategies for mitigating disasters such as the tragic stampede of 2006 that killed over 300 pilgrims during the Hajj. With Qatar set to be one of the major cultural centres in the region, e.g. hosting 2022 FIFA World Cup, the proper control and management of large singular events is paramount for safety and Qatar's standing on the international stage. We aim to use the unique video data gathered from Hajj 2011, to assess the dynamics of very dense crowded environments with a particular focus on dangerous crowd instabilities and systemic shocks. Methods: We make use of increasingly complex optical flow algorithms (Horn-Schunck, Lucas-Kanade, TV-L1) to extract the instantaneous velocity field between each pair of frames in the videos. From these velocity vector fields we then construct the pedestrian (Lagrangian) flow field by the use of texture advection techniques that initially seed the flow with particles or random noise. Results: We present results of the above application of optical flow and texture advection methods to the data we collected in a field study during Hajj 2011. Particularly, we aim to illustrate the specific flow patterns that arise in such crowded environments. We also aim to present the preliminary results of a pilot multiple camera stereovision study conducted in the London Central Mosque on a Friday when the mosque was particularly crowded.

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/content/papers/10.5339/qfarf.2012.CSP7
2012-10-01
2020-09-27
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarf.2012.CSP7
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