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2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04) - Volume 2
Bridging the Gaps between Cameras
Washington, D.C., USA
June 27-July 02
ISBN: 0-7695-2158-4
ASCII Text
x 
 
Dimitrios Makris, Tim Ellis, James Black, "Bridging the Gaps between Cameras," Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, vol. 2, pp. 205-210, 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04) - Volume 2, 2004.
 
 
BibTex
x
 
@article{ 10.1109/CVPR.2004.53,
author = {Dimitrios Makris and Tim Ellis and James Black},
title = {Bridging the Gaps between Cameras},
journal ={Computer Vision and Pattern Recognition, IEEE Computer Society Conference on},
volume = {2},
year = {2004},
issn = {1063-6919},
pages = {205-210},
doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2004.53},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
TI - Bridging the Gaps between Cameras
SN - 1063-6919
SP205
EP210
A1 - Dimitrios Makris,
A1 - Tim Ellis,
A1 - James Black,
PY - 2004
KW - visual surveillance
KW - unsupervised learning
KW - multi-camera tracking
VL - 2
JA - Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
ER -
 
Dimitrios Makris, Kingston University
Tim Ellis, Kingston University
James Black, Kingston University
The paper investigates the unsupervised learning of a model of activity for a multi-camera surveillance network that can be created from a large set of observations. This enables the learning algorithm to establish links between camera views associated with an activity. The learning algorithm operates in a correspondence-free manner, exploiting the statistical consistency of the observation data. The derived model is used to automatically determine the topography of a network of cameras and to provide a means for tracking targets across the "blind" areas of the network. A theoretical justification and experimental validation of the methods are provided.
Index Terms:
visual surveillance, unsupervised learning, multi-camera tracking
Citation:
Dimitrios Makris, Tim Ellis, James Black, "Bridging the Gaps between Cameras," cvpr, vol. 2, pp.205-210, 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04) - Volume 2, 2004
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