@inproceedings{funiak-ipsn06,
author = {Funiak, Stanislav and Guestrin, Carlos and Sukthankar,
Rahul and Paskin, Mark},
title = {Distributed Localization of Networked Cameras},
booktitle = {Fifth International Conference on Information
Processing in Sensor Networks (IPSN'06)},
venue = {International Conference on Information Processing in
Sensor Networks (IPSN'06)},
month = {April},
pages = {34--42},
year = {2006},
keywords = {Probabilistic Inference, Sensing, Distributed
Algorithms, Graphical Models, Localization},
url = {http://www.cs.cmu.edu/~claytronics/papers/funiak-ipsn06.pdf},
abstract = {Camera networks are perhaps the most common type of
sensor network and are deployed in a variety of real-world
applications including surveillance, intelligent environments and
scientific remote monitoring. A key problem in deploying a
network of cameras is calibration, i.e., determining the location
and orientation of each sensor so that observations in an image
can be mapped to locations in the real world. This paper proposes
a fully distributed approach for camera network calibration. The
cameras collaborate to track an object that moves through the
environment and reason probabilistically about which camera poses
are consistent with the observed images. This reasoning employs
sophisticated techniques for handling the difficult
nonlinearities imposed by projective transformations, as well as
the dense correlations that arise between distant cameras. Our
method requires minimal overlap of the cameras' fields of view
and makes very few assumptions about the motion of the object. In
contrast to existing approaches, which are centralized, our
distributed algorithm scales easily to very large camera
networks. We evaluate the system on a real camera network with 25
nodes as well as simulated camera networks of up to 50 cameras
and demonstrate that our approach performs well even when
communication is lossy.},
}
