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"Ninety-nine percent of our crowd monitors themselves," she said.
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Dynamic crowd monitoring was achieved by implementing the context based region identification and grouping of participants, distributed crowd behavior estimation, and stampede prediction based on distributed consensus.
This proposed sensor network consists of mobile devices that are used as crowd monitoring participant nodes that employ light sensors, accelerometers, as well as audio and video sensors to collect the relevant data.
Nowadays, video-based crowd monitoring tools are widely deployed.
[24] review the state-of-the-art of vision-based systems for crowd monitoring.
Thanks to the location-awareness of modern smartphones, they have increasingly been considered as an alternative platform for crowd monitoring.
For the last decades, automatic crowd monitoring in urban areas has mainly been performed by means of image processing [2].
A participatory sensing approach for crowd monitoring faces a major limitation: Participation is based on a voluntary base.
Therefore, [4] presents a privacy preserving approach to video-based crowd monitoring where crowd sizes are estimated without people models or object tracking.
Obtaining knowledge about the current size and density of a crowd is one of the central aspects of crowd monitoring [1].
However, one of the downsides of video-based crowd monitoring is the fact that video cameras tend to be considered as privacy invading.
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