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Principal Axis-Based Correspondence between Multiple Cameras for People Tracking
April 2006 (vol. 28 no. 4)
pp. 663-671
ASCII Text
x 
 
Weiming Hu, Min Hu, Xue Zhou, Tieniu Tan, Jianguang Lou, Steve Maybank, "Principal Axis-Based Correspondence between Multiple Cameras for People Tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 663-671, April, 2006.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2006.80,
author = {Weiming Hu and Min Hu and Xue Zhou and Tieniu Tan and Jianguang Lou and Steve Maybank},
title = {Principal Axis-Based Correspondence between Multiple Cameras for People Tracking},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {28},
number = {4},
issn = {0162-8828},
year = {2006},
pages = {663-671},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.80},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Principal Axis-Based Correspondence between Multiple Cameras for People Tracking
IS - 4
SN - 0162-8828
SP663
EP671
EPD - 663-671
A1 - Weiming Hu,
A1 - Min Hu,
A1 - Xue Zhou,
A1 - Tieniu Tan,
A1 - Jianguang Lou,
A1 - Steve Maybank,
PY - 2006
KW - Correspondence between multiple cameras
KW - principal axes
KW - people tracking.
VL - 28
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Visual surveillance using multiple cameras has attracted increasing interest in recent years. Correspondence between multiple cameras is one of the most important and basic problems which visual surveillance using multiple cameras brings. In this paper, we propose a simple and robust method, based on principal axes of people, to match people across multiple cameras. The correspondence likelihood reflecting the similarity of pairs of principal axes of people is constructed according to the relationship between ”ground-points” of people detected in each camera view and the intersections of principal axes detected in different camera views and transformed to the same view. Our method has the following desirable properties: 1) Camera calibration is not needed. 2) Accurate motion detection and segmentation are less critical due to the robustness of the principal axis-based feature to noise. 3) Based on the fused data derived from correspondence results, positions of people in each camera view can be accurately located even when the people are partially occluded in all views. The experimental results on several real video sequences from outdoor environments have demonstrated the effectiveness, efficiency, and robustness of our method.

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Index Terms:
Correspondence between multiple cameras, principal axes, people tracking.
Citation:
Weiming Hu, Min Hu, Xue Zhou, Tieniu Tan, Jianguang Lou, Steve Maybank, "Principal Axis-Based Correspondence between Multiple Cameras for People Tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 663-671, April 2006, doi:10.1109/TPAMI.2006.80
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