Spatio-temporal tracking from natural language statements using outer probability theory

Bishop, AN; Houssineau, J; Angley, D; Ristic, B

Spatio-temporal tracking from natural language statements using outer probability theory

Bishop, AN

Houssineau, J Angley, D Ristic, B

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Publication Type:: Journal Article
Citation:: Information Sciences, 2018, 463-464 pp. 56 - 74
Issue Date:: 2018-10-01

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Field	Value	Language
dc.contributor.author	Bishop, AN https://orcid.org/0000-0001-8581-8562	en_US
dc.contributor.author	Houssineau, J	en_US
dc.contributor.author	Angley, D	en_US
dc.contributor.author	Ristic, B	en_US
dc.date.issued	2018-10-01	en_US
dc.identifier.citation	Information Sciences, 2018, 463-464 pp. 56 - 74	en_US
dc.identifier.issn	0020-0255	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132709
dc.description.abstract	© 2018 Elsevier Inc. This work considers a target tracking problem where the observed information is in the form of natural language-type statements. More specifically, the focus is on a spatio-temporal tracking problem where each uttered expression may involve both spatial, motion and temporal uncertainty, and a general modelling framework for natural language statements of a rather general semantic form is developed. This framework involves the definition of some tuple that allows one to extract the common semantics from arbitrary parsed expressions conveying some canonical information. Given this tuple, an estimation and tracking method based on the concept of outer probability measures is introduced and an estimation algorithm for handling this temporal uncertainty, along with delayed and out-of-sequence information arrival, is developed. This framework allows for modelling imprecise information in a more general and realistic sense.	en_US
dc.relation.ispartof	Information Sciences	en_US
dc.relation.isbasedon	10.1016/j.ins.2018.06.041	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Spatio-temporal tracking from natural language statements using outer probability theory	en_US
dc.type	Journal Article
utslib.citation.volume	463-464	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	463-464	en_US

Abstract:

© 2018 Elsevier Inc. This work considers a target tracking problem where the observed information is in the form of natural language-type statements. More specifically, the focus is on a spatio-temporal tracking problem where each uttered expression may involve both spatial, motion and temporal uncertainty, and a general modelling framework for natural language statements of a rather general semantic form is developed. This framework involves the definition of some tuple that allows one to extract the common semantics from arbitrary parsed expressions conveying some canonical information. Given this tuple, an estimation and tracking method based on the concept of outer probability measures is introduced and an estimation algorithm for handling this temporal uncertainty, along with delayed and out-of-sequence information arrival, is developed. This framework allows for modelling imprecise information in a more general and realistic sense.

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http://hdl.handle.net/10453/132709