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Abstract

Abstract

With the world's population projected to grow from the current 6.8 billion to around 9 billion by 2050, the resultant increase of megacities and the associated demands on public transport, there is an urgent imperative to understand the dynamics of crowded environments. Very dense crowds that exceed 3 people per square metre present many challenges for efficiently measuring quantities such as density and pedestrian trajectories. The Hajj and the associated minor Muslim pilgrimage of Umrah, present some of the most densely crowded human environments in the world, and thus present an excellent observational laboratory for the study of dense crowd dynamics. An accurate characterisation of such dense crowds cannot only improve existing models, but can also help to develop better intervention strategies for mitigating crowd disasters such as the Hajj 2006 Jamarat stampede that killed over 300 pilgrims. With Qatar set to be one of the cultural centres in the region, e.g. FIFA World Cup 2022, the proper control and management of large singular events is important for not only our safety but also our standing in the international stage.

To use the data gathered from the Hajj to assess the dynamics of large dense crowds with a particular focus on crowd instabilities and pattern formation.

We will make use of advanced image processing and pattern recognition techniques (mathematical morphology, feature selection etc.) in assessing some of the bulk properties of crowds such as density and flow, as well as the finer details such as the ensemble of pedestrian trajectories. We are currently in the process of taking multiple wide-angle stereo videos at this year's Hajj, with our collaborators in Umm Al-Qurra University in Mecca. Multiple video capture of the same scene from different angles allows one to overcome the problem of occlusion in dense crowds.

We will present our field study in the Hajj this year, where we took extensive high quality multiple camera video data. We will also present some of the techniques, which we will be using over the coming year in analyzing this large data set that we have now successfully collated.

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/content/papers/10.5339/qfarf.2011.CSP17
2011-11-20
2020-06-06
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarf.2011.CSP17
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