- Research Article
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Determining Vision Graphs for Distributed Camera Networks Using Feature Digests
EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 057034 (2006)
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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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
- False Alarm
- Feature Point
- False Alarm Rate
- Camera Calibration
- High Detection Rate