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Image-based Anomaly Detection within Crowds

Golda, Thomas

Authorities and security services have to deal with more and more data collected during events and on public places. Two reasons for that are the rising number of huge events, as well as the expanding coverage with CCTV cameras of areas within cities. Even the number of ground crew teams, that are equipped with mobile cameras, rises continuously. These examples show that modern surveillance and location monitoring systems come with need of suited assistance systems, which help the associated security workers to keep track of the situations. In this report, we present a first idea how such a system using modern machine
learning algorithms could look like. Furthermore, a more detailed look on two state-of-the-art methods for human pose estimation is given. These algorithms are then investigated for their performance on the target domain of crowd surveillance scenarios using a small dataset called CrowdPose.

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Verlagsausgabe §
DOI: 10.5445/IR/1000097082
Veröffentlicht am 02.08.2019
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2019
Sprache Englisch
Identifikator ISBN: 978-3-7315-0936-3
ISSN: 1863-6489
KITopen-ID: 1000097082
Erschienen in Proceedings of the 2018 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. Ed.: J. Beyerer, M. Taphanel
Verlag KIT Scientific Publishing
Seiten 11-24
Serie Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe ; 40
Bemerkung zur Veröffentlichung Technical Report IES-2018-02
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