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Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007)
A Multi-object Motion-tracking Method for Video Surveillance
Haier International Training Center, Qingdao, China
July 30-August 01
ISBN: 0-7695-2909-7
ASCII Text
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Jiang Dan, Yu Yuan, "A Multi-object Motion-tracking Method for Video Surveillance," Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on, vol. 1, pp. 402-405, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007.
 
 
BibTex
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@article{ 10.1109/SNPD.2007.17,
author = {Jiang Dan and Yu Yuan},
title = {A Multi-object Motion-tracking Method for Video Surveillance},
journal ={Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on},
volume = {1},
year = {2007},
isbn = {0-7695-2909-7},
pages = {402-405},
doi = {http://doi.ieeecomputersociety.org/10.1109/SNPD.2007.17},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
TI - A Multi-object Motion-tracking Method for Video Surveillance
SN - 0-7695-2909-7
SP402
EP405
A1 - Jiang Dan,
A1 - Yu Yuan,
PY - 2007
KW - Motion Tracking
KW - Foreground Detection similarity function
VL - 1
JA - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
ER -
 
Jiang Dan, Beihang University, China
Yu Yuan, Beihang University, China
In this paper, a multi-object motion-tracking method based both on region and feature tracking is proposed for the purpose of real-time tracking in video surveillance system. Moving object is detected through foreground detection. Then we extract three features of each moving objects such as centroid, area, and average luminance. At last, the similarity function is applied to tracking. It is proved that the method has good performance under dynamic circumstances for real-time tracking.
Index Terms:
Motion Tracking, Foreground Detection similarity function
Citation:
Jiang Dan, Yu Yuan, "A Multi-object Motion-tracking Method for Video Surveillance," snpd, vol. 1, pp.402-405, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007
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