Learning models of activities involving interacting objects

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Dokumenter

We propose the LEMAIO multi-layer framework, which makes use of hierarchical
abstraction to learn models for activities involving multiple interacting objects
from time sequences of data concerning the individual objects.
Experiments in the sea navigation domain yielded learned models that were then successfully applied to activity recognition, activity simulation and multi-target tracking. Our method compares favourably with respect to previously reported results using Hidden Markov Models and Relational Particle Filtering.
OriginalsprogEngelsk
TitelAdvances in Intelligent Data Analysis XII : 12th International Symposium, IDA 2013, London, UK, October 17-19, 2013, Proceedings
RedaktørerAllan Tucker, Frank Höppner, Arno Siebes, Stephen Swift
Antal sider13
ForlagSpringer
Publikationsdato2013
Sider285-297
ISBN (Trykt)978-3-642-41397-1
ISBN (Elektronisk)978-3-642-41398-8
DOI
StatusUdgivet - 2013
Begivenhed12th International Symposium on Advances in Intelligent Data Analysis - London, Storbritannien
Varighed: 17 okt. 201319 okt. 2013
Konferencens nummer: 12

Konference

Konference12th International Symposium on Advances in Intelligent Data Analysis
Nummer12
LandStorbritannien
ByLondon
Periode17/10/201319/10/2013
NavnLecture notes in computer science
Vol/bind8207

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