Learning models of activities involving interacting objects

Cristina Manfredotti, Kim Steenstrup Pedersen, Howard J. Hamilton, Sandra Zilles

1 Citation (Scopus)
53 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publicationAdvances in Intelligent Data Analysis XII : 12th International Symposium, IDA 2013, London, UK, October 17-19, 2013, Proceedings
EditorsAllan Tucker, Frank Höppner, Arno Siebes, Stephen Swift
Number of pages13
PublisherSpringer
Publication date2013
Pages285-297
ISBN (Print)978-3-642-41397-1
ISBN (Electronic)978-3-642-41398-8
DOIs
Publication statusPublished - 2013
Event12th International Symposium on Advances in Intelligent Data Analysis - London, United Kingdom
Duration: 17 Oct 201319 Oct 2013
Conference number: 12

Conference

Conference12th International Symposium on Advances in Intelligent Data Analysis
Number12
Country/TerritoryUnited Kingdom
CityLondon
Period17/10/201319/10/2013
SeriesLecture notes in computer science
Volume8207

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