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Task-oriented situation recognition

: Bauer, A.; Fischer, Y.

Postprint urn:nbn:de:0011-n-1332774 (909 KByte PDF)
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Copyright 2010 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Created on: 16.6.2010

Buford, J.F. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.; Society for Imaging Science and Technology -IS&T-:
Cyber security, situation management, and impact assessment II; and visual analytics for homeland defense and security II : 5 and 8-9 April 2010, Orlando, Florida, USA
Bellingham, WA: SPIE, 2010 (Proceedings of SPIE 7709)
ISBN: 978-0-8194-8173-3
Paper 770906
Conference "Cyber Security, Situation Management, and Impact Assessment" <2, 2010, Orlando/Fla.>
Conference "Visual Analytics for Homeland Defense and Security <2, 2010, Orlando/Fla.>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

From the advances in computer vision methods for the detection, tracking and recognition of objects in video streams, new opportunities for video surveillance arise: In the future, automated video surveillance systems will be able to detect critical situations early enough to enable an operator to take preventive actions, instead of using video material merely for forensic investigations. However, problems such as limited computational resources, privacy regulations and a constant change in potential threads have to be addressed by a practical automated video surveillance system. In this paper, we show how these problems can be addressed using a task-oriented approach. The system architecture of the task-oriented video surveillance system NEST and an algorithm for the detection of abnormal behavior as part of the system are presented and illustrated for the surveillance of guests inside a video-monitored building.