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Knowledge-based situational analysis of unusual events in public places

: Münch, David; Becker, Stefan; Kieritz, Hilke; Hübner, Wolfgang; Arens, Michael

Volltext urn:nbn:de:0011-n-3624980 (830 KByte PDF)
MD5 Fingerprint: 24bfc748eab94c62f5a12d0f89afe186
Erstellt am: 13.10.2015

Beyerer, Jürgen (Ed.); Meissner, Andreas (Ed.); Geisler, Jürgen (Ed.) ; Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung -IOSB-, Karlsruhe:
10th Future Security 2015. Security Research Conference. Proceedings : September 15 – 17, 2015, Berlin
Stuttgart: Fraunhofer Verlag, 2015
ISBN: 978-3-8396-0908-8
ISBN: 3-8396-0908-9
Security Research Conference "Future Security" <10, 2015, Berlin>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
left luggage detection; video surveillance; situation recognition

Combining appropriate methods from computer vision and artificial intelligence enables further progress in smart video surveillance. In this work, an Interacting Multiple Model (IMM) filter is used for person tracking due to the fact that a single motion may not capture the complex dynamics from persons. In addition, context information from the IMM is used for controlling the background model to detect left luggage. The combination of this processing chain serves as input for the situation recognition in addition to person detection and tracking. The computer vision components are integrated in the distributed Cognitive Vision System (dCVS) architecture, which is applied up to now to Traffic, Robotics, Smart Homes, and Video Surveillance. For this work, we cope with situations dealing with unusual events in public places