Learning models of activities involving interacting objects
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- Learning models of activities involving interacting objects
Accepted author manuscript, 804 KB, PDF document
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.
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 language | English |
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Title of host publication | Advances in Intelligent Data Analysis XII : 12th International Symposium, IDA 2013, London, UK, October 17-19, 2013, Proceedings |
Editors | Allan Tucker, Frank Höppner, Arno Siebes, Stephen Swift |
Number of pages | 13 |
Publisher | Springer |
Publication date | 2013 |
Pages | 285-297 |
ISBN (Print) | 978-3-642-41397-1 |
ISBN (Electronic) | 978-3-642-41398-8 |
DOIs | |
Publication status | Published - 2013 |
Event | 12th International Symposium on Advances in Intelligent Data Analysis - London, United Kingdom Duration: 17 Oct 2013 → 19 Oct 2013 Conference number: 12 |
Conference
Conference | 12th International Symposium on Advances in Intelligent Data Analysis |
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Nummer | 12 |
Land | United Kingdom |
By | London |
Periode | 17/10/2013 → 19/10/2013 |
Series | Lecture notes in computer science |
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Volume | 8207 |
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