Monitoring Crowd Condition in Public Spaces by Tracking Mobile Consumer Devices with Wifi Interface

Jens Weppner, Benjamin Bischke, Paul Lukowicz

In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp-16) September 12-16 New York, NY, USA Germany Pages 1363-1371 UbiComp '16 ISBN 978-1-4503-4462-3 ACM 2016.


We present a systematic study and optimization of crowd monitoring methods based on tracking consumer devices with activated WiFi/Bluetooth interfaces using stationary scanners with directional antennas. To this end we have recorded a large scale, real life data set from a car manu- facturers exhibition at the Frankfurt Motor Show IAA that includes data from 31 directional scanners covering a total area of 6000m^2 running for 13 business days and providing nearly 90 million data points from a total of over 300000 unique mobile devices. For seven of the 13 days video ground truth has been recorded and extensively annotated. Questions that we addressed include the map- ping from the number of detected devices to the number of people, the ability to generalize the calibration from a small number of ground truth points recorded on one day to other days and the ability to localize individuals in differ- ent conditions. Our methods show less than 20% error for the crowd density and less than 8 m localization error for individuals.

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German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz