Manfredotti, Cristina3; Pedersen, Kim Steenstrup5; Hamilton, Howard J.6; Zilles, Sandra6
Allan Tucker, Frank Höppner, Arno Siebes, Stephen Swift
1 Administration, Department of Computer Science, Faculty of Science, Københavns Universitet2 The Image Section, Department of Computer Science, Faculty of Science, Københavns Universitet3 Pierre and Marie Curie University (UPMC)4 University of Regina5 Administration, Department of Computer Science, Faculty of Science, Københavns Universitet6 University of Regina
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.
Advances in Intelligent Data Analysis Xii: 12th International Symposium, Ida 2013, London, Uk, October 17-19, 2013, Proceedings, 2013, p. 285-297
Main Research Area:
Lecture Notes in Computer Science
12th International Symposium on Advances in Intelligent Data AnalysisIntelligent Data Analysis, 2013