PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning switching dynamic models for objects tracking
Gilles Celeux, J. Nascimento and J. Marques
Pattern Recognition Number 37, pp. 1841-1853, 2004.

Abstract

Many recent tracking algorithms rely on model learning methods. A promising approach consists of modelling the object motion with switching autoregressive models. This article is involved with parametric switching dynamical models governed by an hidden Markov chain. The maximum likelihood estimation of the parameters of those models is described. The formulas of the EM algorithm are detailed. Moreover, the problem of choosing a good and parsimonious model with BIC criterion is considered. Emphasis is put on choosing a reasonable number of hidden states. Numerical experiments on both simulated and real data sets highlight the ability of this approach to describe properly object motions with sudden changes. The two appplications on real data concern object and heart tracking.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:651
Deposited By:Michele Sebag
Deposited On:29 December 2004