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Multiple Model Tracking by Imprecise Markov Trees Alessandro Antonucci and Alessio Benavoli and Marco Zaffalon
 

Summary: Multiple Model Tracking by Imprecise Markov Trees
Alessandro Antonucci and Alessio Benavoli and Marco Zaffalon
IDSIA, Switzerland
{alessandro,alessio,zaffalon}@idsia.ch
Gert de Cooman and Filip Hermans
SYSTeMS, Ghent University, Belgium
{gert.decooman,filip.hermans}@ugent.be
Abstract We present a new procedure for tracking
manoeuvring objects by hidden Markov chains. It leads
to more reliable modelling of the transitions between
hidden states compared to similar approaches proposed
within the Bayesian framework: we adopt convex sets
of probability mass functions rather than single `precise
probability' specifications, in order to provide a more re-
alistic and cautious model of the manoeuvre dynamics.
In general, the downside of such increased freedom in
the modelling phase is a higher inferential complexity.
However, the simple topology of hidden Markov chains
allows for efficient tracking of the object through a re-
cently developed belief propagation algorithm. Further-

  

Source: Antonucci, Alessandro - Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA)
Zaffalon, Marco - Istituto Dalle Molle di Studi sull' Intelligenza Artificiale (IDSIA)

 

Collections: Computer Technologies and Information Sciences