Coenosense: A framework for real-time detection and visualization of collective behaviors in human crowds by tracking mobile devices

Martin Wirz, Tobias Franke, Eve Mitleton-Kelly, Daniel Roggen, Paul Lukowicz, Gerhard Tröster

In: Proceedings of the European Conference on Complex Systems 2012. European Conference on Complex Systems (ECCS-2012) Brussels Belgium Seiten 353-361 ISBN 978-3-319-00394-8 Springer Heidelberg 2013.


There is a need for event organizers and emergency response personnel to detect emerging, potentially critical crowd situations at an early stage during city-wide mass gatherings. In this work, we present a framework to infer and visualize crowd behavior patterns in real-time from pedestrians’ GPS location traces. We deployed and tested our framework during the 2011 Lord Mayor’s Show in London. To collection location updates from festival visitors, a mobile phone app that supplies the user with event-related information and periodically logs the device’s location was distributed. We collected around four million location updates from over 800 visitors. The City of London Police consulted the crowd condition visualization to monitor the event. We learned from the police officers that our framework helps to assess occurring crowd conditions and to spot critical situations faster compared to the traditional video-based methods. With that, appropriate measure can be deployed quickly helping to resolve a critical situation at an early stage.

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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence