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Inter-Frame Change Directing online clustering of multiple moving objects for video-based sensor networks

Huang, Guangyan, He, Jing and Ding, Zhiming 2008, Inter-Frame Change Directing online clustering of multiple moving objects for video-based sensor networks, in WI-IAT 2008: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, IEEE, Piscataway, N.J., pp. 442-446, doi: 10.1109/WIIAT.2008.125.

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Title Inter-Frame Change Directing online clustering of multiple moving objects for video-based sensor networks
Author(s) Huang, Guangyan
He, Jing
Ding, Zhiming
Conference name IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops (2008 : Sydney, N.S.W)
Conference location Sydney, N.S.W.
Conference dates 9-12 Dec. 2008
Title of proceedings WI-IAT 2008: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops
Publication date 2008
Start page 442
End page 446
Total pages 5
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Recognition of multiple moving objects is a very important task for achieving user-cared knowledge to send to the base station in wireless video-based sensor networks. However, video based sensor nodes, which have constrained resources and produce huge amount of video streams continuously, bring a challenge to segment multiple moving objects from the video stream online. Traditional efficient clustering algorithms such as DBSCAN cannot run time-efficiently and even fail to run on limited memory space on sensor nodes, because the number of pixel points is too huge. This paper provides a novel algorithm named Inter-Frame Change Directing Online clustering (IFCDO clustering) for segmenting multiple moving objects from video stream on sensor nodes. IFCDO clustering only needs to group inter-frame different pixels, thus it reduces both space and time complexity while achieves robust clusters the same as DBSCAN. Experiment results show IFCDO clustering excels DBSCAN in terms of both time and space efficiency. © 2008 IEEE.
ISBN 9780769534961
Language eng
DOI 10.1109/WIIAT.2008.125
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083682

Document type: Conference Paper
Collection: School of Information Technology
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