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Analyzing pedestrian behavior in crowds for automatic detection of congestions

: Krausz, Barbara; Bauckhage, Chrisitan

Postprint urn:nbn:de:0011-n-1926757 (867 KByte PDF)
MD5 Fingerprint: f0ec89089bd4aaa47fc42c4cc7722174
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Created on: 26.1.2012

Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Conference on Computer Vision, ICCV Workshops 2011 : 6-13 November 2011, Barcelona, Spain
Piscataway/NJ: IEEE, 2011
ISBN: 978-1-4673-0062-9 (online)
ISBN: 978-1-4673-0063-6
Workshop on Modeling, Simulation and Visual Analysis of Large Crowds <1, 2011, Barcelona>
International Conference on Computer Vision (ICCV) <13, 2011, Barcelona>
Conference Paper, Electronic Publication
Fraunhofer IAIS ()
video surveillance; crowd monitoring; congestion; mass behavior

Congestions in pedestrian traffic typically occur when the number of pedestrians exceeds the capacity of pedestrian facilities. In some cases, the pedestrian density reaches a critical level which may lead to a crowd stampede as happens rather frequently at mass gatherings, in stadiums or at train stations. In the past, research has focused on improving simulations of crowd motion in order to identify potentially dangerous locations and to direct pedestrian streams. Recently, works towards the automatic real-time detection of critical mass behavior based on optical flow computations have been proposed. In this paper, we verify these approaches by analyzing mircoscopic pedestrian behavior in congestions and conducting experiments on synthetic as well as on real datasets.