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  • Research Article
  • Open Access

Determining Vision Graphs for Distributed Camera Networks Using Feature Digests

EURASIP Journal on Advances in Signal Processing20062007:057034

https://doi.org/10.1155/2007/57034

  • Received: 4 January 2006
  • Accepted: 18 May 2006
  • Published:

Abstract

We propose a decentralized method for obtaining the vision graph for a distributed, ad-hoc camera network, in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. Each camera encodes a spatially well-distributed set of distinctive, approximately viewpoint-invariant feature points into a fixed-length "feature digest" that is broadcast throughout the network. Each receiver camera robustly matches its own features with the decompressed digest and decides whether sufficient evidence exists to form a vision graph edge. We also show how a camera calibration algorithm that passes messages only along vision graph edges can recover accurate 3D structure and camera positions in a distributed manner. We analyze the performance of different message formation schemes, and show that high detection rates ( ) can be achieved while maintaining low false alarm rates ( ) using a simulated 60-node outdoor camera network.

Keywords

  • False Alarm
  • Feature Point
  • False Alarm Rate
  • Camera Calibration
  • High Detection Rate

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Authors’ Affiliations

(1)
Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA

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Copyright

© Zhaolin Cheng et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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