Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

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
© 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IITB ( IOSB) ()

Abstract
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.

: http://publica.fraunhofer.de/dokumente/N-111528.html