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High-level situation recognition using fuzzy metric temporal logic, case studies in surveillance and smart environments

: Münch, David; Ijsselmuiden, Joris; Arens, Michael; Stiefelhagen, Rainer

Volltext urn:nbn:de:0011-n-1857918 (4.7 MByte PDF)
MD5 Fingerprint: d96759049c0405e4cd77440bd6010100
Erstellt am: 23.11.2011

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
International Conference on Computer Vision (ICCV) <13, 2011, Barcelona>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Although computer vision and other machine perception have made great progress in recent years, corresponding high-level components have not progressed that fast. We present a general purpose framework for high-level situation recognition that is suited for arbitrary application domains and sensor setups. Our approach is hierarchical as opposed to monolithic and we focus on modeling expert knowledge with Fuzzy Metric Temporal Logic and Situation Graph Trees rather than learning from training data. To demonstrate the power and flexibility of our approach, we present case studies in two different settings: guiding the operator's attention in video surveillance and automatic report generation in smart environments. Our results show that this approach can yield a conceptually exhaustive situation recognition for diverse input modalities and application domains.