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Determining Vision Graphs for Distributed Camera Networks Using Feature Digests

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.

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Correspondence to Zhaolin Cheng.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Cheng, Z., Devarajan, D. & Radke, R.J. Determining Vision Graphs for Distributed Camera Networks Using Feature Digests. EURASIP J. Adv. Signal Process. 2007, 057034 (2006). https://doi.org/10.1155/2007/57034

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Keywords

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