EURASIP Journal on Advances in Signal Processing
Volume 2007 (2007), Article ID 57034, 11 pages
doi:10.1155/2007/57034
Research Article
Determining Vision Graphs for Distributed Camera Networks Using Feature Digests
Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy 12180, NY, USA
Received 4 January 2006; Revised 18 April 2006; Accepted 18 May 2006
Academic Editor: Deepa Kundur
Copyright © 2007 Zhaolin Cheng et al. 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.
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 (>0.8) can be
achieved while maintaining low false alarm rates (<0.05) using a
simulated 60-node outdoor camera network.