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Automated multi-camera planar tracking correspondence modeling
Madison, Wisconsin June 18-June 20
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2003.12113622003 IEEE Computer Society Conference ...
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Chris Stauffer, Kinh Tieu, "Automated multi-camera planar tracking correspondence modeling," Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, vol. 1, pp. 259, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03) - Volume 1, 2003.
 
 
BibTex
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@article{ 10.1109/CVPR.2003.1211362,
author = {Chris Stauffer and Kinh Tieu},
title = {Automated multi-camera planar tracking correspondence modeling},
journal ={Computer Vision and Pattern Recognition, IEEE Computer Society Conference on},
volume = {1},
year = {2003},
issn = {1063-6919},
pages = {259},
doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2003.1211362},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
TI - Automated multi-camera planar tracking correspondence modeling
SN - 1063-6919
SP
EP
A1 - Chris Stauffer,
A1 - Kinh Tieu,
PY - 2003
KW - null
VL - 1
JA - Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
ER -
 
Chris Stauffer, Massachusetts Institute of Technology
Kinh Tieu, Massachusetts Institute of Technology
This paper introduces a method for robustly estimating a planar tracking correspondence model (TCM) for a large camera network directly from tracking data and for employing said model to reliably track objects through multiple cameras. By exploiting the unique characteristics of tracking data, our method can reliably estimate a planar TCM in large environments covered by many cameras. It is robust to scenes with multiple simultaneously moving objects and limited visual overlap between the cameras. Our method introduces the capability of automatic calibration of large camera networks in which the topology of camera overlap is unknown and in which all cameras do not necessarily overlap. Quantitative results are shown for a five camera network in which the topology is not specified.
Citation:
Chris Stauffer, Kinh Tieu, "Automated multi-camera planar tracking correspondence modeling," cvpr, vol. 1, pp.259, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03) - Volume 1, 2003
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