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Snapshot: A Self-Calibration Protocol for Camera Sensor Networks

Authors: Liu, XT; Kulkarni, P; Shenoy, P; Ganesan, D;

Snapshot: A Self-Calibration Protocol for Camera Sensor Networks

Abstract

A camera sensor network is a wireless network of cameras designed for ad-hoc deployment. The camera sensors in such a network need to be properly calibrated by determining their location, orientation, and range. This paper presents Snapshot, an automated calibration protocol that is explicitly designed and optimized for camera sensor networks. Snapshot uses the inherent imaging abilities of the cameras themselves for calibration and can determine the location and orientation of a camera sensor using only four reference points. Our techniques draw upon principles from computer vision, optics, and geometry and are designed to work with low-fidelity, low-power camera sensors that are typical in sensor networks. An experimental evaluation of our prototype implementation shows that Snapshot yields an error of 1-2.5 degrees when determining the camera orientation and 5-10cm when determining the camera location. We show that this is a tolerable error in practice since a Snapshot-calibrated sensor network can track moving objects to within 11cm of their actual locations. Finally, our measurements indicate that Snapshot can calibrate a camera sensor within 20 seconds, enabling it to calibrate a sensor network containing tens of cameras within minutes.

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United States
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    popularity
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    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Average
Top 10%
Top 10%
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