Privacy-Preserving Crowd Incident Detection: A Holistic Experimental Approach

Abstract : Detecting dangerous situations is crucial for emergency management. Surveillance systems detect dangerous situations by analyzing crowd dynamics. This paper presents a holis-tic 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 multi-camera 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 work-flow 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.
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Communication dans un congrès
ACM SIGSPATIAL Workshop on the Use of GIS in Emergency Management (EM-GIS-2015), Nov 2015, Seattle, United States
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Emmanuel Baccelli, Alexandra Danilkina, Sebastian Müller, Agnès Voisard, Matthias Wählisch. Privacy-Preserving Crowd Incident Detection: A Holistic Experimental Approach. ACM SIGSPATIAL Workshop on the Use of GIS in Emergency Management (EM-GIS-2015), Nov 2015, Seattle, United States. 〈hal-01244673〉

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