Layered dynamic probabilistic networks for spatio-temporal modelling

Bui, Hung H., Venkatesh, Svetha and West, Geoff 1999, Layered dynamic probabilistic networks for spatio-temporal modelling, Intelligent data analysis, vol. 3, no. 5, pp. 339-361.

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Title Layered dynamic probabilistic networks for spatio-temporal modelling
Author(s) Bui, Hung H.
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
West, Geoff
Journal name Intelligent data analysis
Volume number 3
Issue number 5
Start page 339
End page 361
Total pages 23
Publisher IOS Press
Place of publication Amsterdam, The Netherlands
Publication date 1999
ISSN 1088-467X
Keyword(s) dynamic probabilistic networks
reasoning with different levels of abstraction
wide-area surveillance
Summary In applications such as tracking and surveillance in large spatial environments, there is a need for representing dynamic and noisy data and at the same time dealing with them at different levels of detail. In the spatial domain, there has been work dealing with these two issues separately, however, there is no existing common framework for dealing with both of them. In this paper, we propose a new representation framework called the Layered Dynamic Probabilistic Network (LDPN), a special type of Dynamic Probabilistic Network (DPN), capable of handling uncertainty and representing spatial data at various levels of detail. The framework is thus particularly suited to applications in wide-area environments which are characterised by large region size, complex spatial layout and multiple sensors/cameras. For example, a building has three levels: entry/exit to the building, entry/exit between rooms and moving within rooms. To avoid the problem of a relatively large state space associated with a large spatial environment, the LDPN explicitly encodes the hierarchy of connected spatial locations, making it scalable to the size of the environment being modelled. There are three main advantages of the LDPN. First, the reduction in state space makes it suitable for dealing with wide area surveillance involving multiple sensors. Second, it offers a hierarchy of intervals for indexing temporal data. Lastly, the explicit representation of intermediate sub-goals allows for the extension of the framework to easily represent group interactions by allowing coupling between sub-goal layers of different individuals or objects. We describe an adaptation of the likelihood sampling inference scheme for the LDPN, and illustrate its use in a hypothetical surveillance scenario.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©1999, IOS Press
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