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Multisensor benchmark data for riot control

 
: Jäger, U.E.; Höpken, M.; Dürr, B.; Metzler, J.; Willersinn, D.

:
Preprint urn:nbn:de:0011-n-844402 (596 KByte PDF)
MD5 Fingerprint: f016957324c4446d319f1ee3c03e437d
Copyright 2008 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: 28.8.2009


Kamerman, G.W. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Electro-optical remote sensing, photonic technologies, and applications II : Cardiff, 15-16 September 2008
Bellingham, WA: SPIE, 2008 (Proceedings of SPIE 7114)
ISBN: 978-0-8194-7346-2
ISSN: 0277-786X
Paper 711403
Conference "Electro-Optical Remote Sensing, Photonic Technologies, and Applications" <2, 2008, Cardiff>
English
Conference Paper, Electronic Publication
Fraunhofer IITB ( IOSB) ()

Abstract
Quick and precise response is essential for riot squads when coping with escalating violence in crowds. Often it is just a single person, known as the leader of the gang, who instigates other people and thus is responsible of excesses. Putting this single person out of action in most cases leads to a de-escalating situation. Fostering de-escalations is one of the main tasks of crowd and riot control. To do so, extensive situation awareness is mandatory for the squads and can be promoted by technical means such as video surveillance using sensor networks. To develop software tools for situation awareness appropriate input data with well-known quality is needed. Furthermore, the developer must be able to measure algorithm performance and ongoing improvements. Last but not least, after algorithm development has finished and marketing aspects emerge, meeting of specifications must be proved. This paper describes a multisensor benchmark which exactly serves this purpose. We first define the underlying algorithm task. Then we explain details about data acquisition and sensor setup and finally we give some insight into quality measures of multisensor data. Currently, the multisensor benchmark described in this paper is applied to the development of basic algorithms for situational awareness, e.g. tracking of individuals in a crowd.

: http://publica.fraunhofer.de/documents/N-84440.html