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Privacy-preserving crowd incident detection: A holistic experimental approach

: Baccelli, Emmanuel; Danilkina, Alexandra; Müller, Sebastian; Voisard, Agnes; Wählisch, Matthias


Association for Computing Machinery -ACM-; Association for Computing Machinery -ACM-, Special Interest Group on Spatial Information -SIGSPATIAL-:
EM-GIS 2015, 1st ACM SIGSPATIAL International Workshop on the Use of GIS in Emergency Management. Proceedings : 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, Washington, USA, November 3-6, 2015
New York: ACM, 2015
ISBN: 978-1-4503-3970-4
Art. 8
International Workshop on the Use of GIS in Emergency Management (EM-GIS) <1, 2015, Seattle/Wash.>
International Conference on Advances in Geographic Information Systems (GIS) <23, 2015, Seattle/Wash.>
European Commission EC
H2020; 653747; City.Risks
Conference Paper
Fraunhofer FOKUS ()

Detecting dangerous situations is crucial for emergency management. Surveillance systems detect dangerous situations by analyzing crowd dynamics. This paper presents a holistic video-based approach for privacy-preserving crowd density estimation. Our experimental approach leverages distributed, on-board pre-processing, allowing privacy as well as the use of low-power, low-throughput wireless communications to interconnect cameras. We developed a multicamera grid-based people counting algorithm which provides the density per cell for an overall view on the monitored area. This view comes from a merger of infrared and Kinect camera data. We describe our approach using a layered model for data aggregation and abstraction together with a workflow model for the involved software components, focusing on their functionality. The power of our approach is illustrated through the real-world experiment that we carried out at the Schönefeld airport in the city of Berlin.