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Image-based Anomaly Detection within Crowds

: Golda, Thomas

Fulltext urn:nbn:de:0011-n-5521907 (11 MByte PDF)
MD5 Fingerprint: e7081ccae535f1c55e3a2ffc4b68181a
Created on: 19.7.2019

Beyerer, Jürgen (Ed.); Taphanel, Miro (Ed.); Taphanel, Miro (Ed.):
Proceedings of the 2018 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
Karlsruhe: KIT Scientific Publishing, 2019 (Karlsruher Schriften zur Anthropomatik 40)
ISBN: 978-3-7315-0936-3
ISBN: 3-7315-0936-9
DOI: 10.5445/KSP/1000094782
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation and Institute for Anthropomatics, Vision and Fusion Laboratory (Joint Workshop) <2018, Triberg-Nussbach>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

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.