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Classifying and detecting group behaviour from visual surveillance data

Hosie, Robin, Venkatesh, Svetha and West, Geoff 1998, Classifying and detecting group behaviour from visual surveillance data, in ICPR 1998 : Proceedings of the 14th International Conference on Pattern Recognition, IEEE, Los Alamitos, Calif., pp. 602-604.

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Title Classifying and detecting group behaviour from visual surveillance data
Author(s) Hosie, Robin
Venkatesh, Svetha
West, Geoff
Conference name International Conference on Pattern Recognition (14th : 1998 : Brisbane, Qld.)
Conference location Brisbane, Qld.
Conference dates 16-20 Aug. 1998
Title of proceedings ICPR 1998 : Proceedings of the 14th International Conference on Pattern Recognition
Editor(s) Jain, Anil K.
Venkatesh, Svetha
Lovell, Brian Carrington
Publication date 1998
Conference series International Conference on Pattern Recognition
Start page 602
End page 604
Total pages 3
Publisher IEEE
Place of publication Los Alamitos, Calif.
Keyword(s) group behaviour detection
image segmentation
learning systems
pair primitives
pairwise movement patterns
pattern classification
target tracking
temporal sequence
visual surveillance system
Summary We outline an approach to classifying and detecting behaviours from surveillance data. Simple pairwise movement patterns are learned and used as building blocks to describe behaviour over a temporal sequence, or compared with other pairs to detect group behaviour. As the pair primitives are easy to redefine and learn, and complex behaviour over time is specified by the user as a sequence of pair primitives, this approach provides a flexible yet robust method of detecting complex movement in a wide variety of domains.
ISBN 0818685131
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©1998, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044890

Document type: Conference Paper
Collection: School of Information Technology
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