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A knowledge-based camera selection approach for object tracking in large sensor networks

: Monari, Eduardo; Kroschel, Kristian

Postprint urn:nbn:de:0011-n-1115285 (677 KByte PDF)
MD5 Fingerprint: 9adf487d5f4cb80c211f49b85588b79e
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Erstellt am: 12.2.2010

Association for Computing Machinery -ACM-; Institute of Electrical and Electronics Engineers -IEEE-:
3rd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2009. CD-ROM : 30 august - 2 september, 2009 Como (Italy)
New York, NY: IEEE, 2009
ISBN: 978-1-4244-4620-9
8 S.
International Conference on Distributed Smart Cameras (ICDSC) <3, 2009, Como>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IITB ( IOSB) ()

In this paper an approach for dynamic sensor selection in large video-based sensor networks for the purpose of multi-camera object tracking is presented. The sensor selection approach is based on computational geometry algorithms and is able to determine task-relevant cameras (camera cluster) by evaluation of geometrical attributes, given the last observed object position, the sensor configurations and the environment model. Hereby, a special goal of this algorithm is to determine the minimum number of sensors needed to relocate an object, even if the object is temporarily out of sight (e.g., by non-overlapping sensor coverage). It will be shown that the algorithm enables self-organizing tracking approaches to perform optimal camera selection in a highly efficient way. In particular, the approach is applicable to very large camera networks and leads to a highly reduced network and processor load for multi-camera tracking.