Calibrating Distributed Camera Networks

Recent developments in wireless sensor networks have made feasible distributed camera networks, in which cameras and processing nodes may be spread over a wide geographical area, with no centralized processor and limited ability to communicate a large amount of information over long distances. This...

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Bibliographic Details
Published in:Proceedings of the IEEE 2008-10, Vol.96 (10), p.1625-1639
Main Authors: Devarajan, Dhanya, Cheng, Zhaolin, Radke, Richard J.
Format: Article
Language:English
Subjects:
Algorithms
Belief propagation
bundle adjustment
Calibration
camera calibration
Cameras
Computer vision
Digital cameras
Distributed algorithms
Estimates
Graphs
metric reconstruction
Networks
Refining
Robustness
sensor networks
structure from motion
Studies
Surveillance
Systems engineering and theory
Vision
Wireless sensor networks
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Summary:Recent developments in wireless sensor networks have made feasible distributed camera networks, in which cameras and processing nodes may be spread over a wide geographical area, with no centralized processor and limited ability to communicate a large amount of information over long distances. This paper overviews distributed algorithms for the calibration of such camera networks- that is, the automatic estimation of each camera's position, orientation, and focal length. In particular, we discuss a decentralized method for obtaining the vision graph for a distributed camera network, in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. We next describe a distributed algorithm in which each camera performs a local, robust nonlinear optimization over the camera parameters and scene points of its vision graph neighbors in order to obtain an initial calibration estimate. We then show how a distributed inference algorithm based on belief propagation can refine the initial estimate to be both accurate and globally consistent.
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2008.928759