Name
Abstract | ||
|---|---|---|
Many recent tracking algorithms rely on model learning methods. A promising approach consists of modeling 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 applications on real data concern object and heart tracking. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.patcog.2004.01.020 | Pattern Recognition |
Keywords | Field | DocType |
Auto regressive model,Hidden Markov chain,EM algorithm,BIC criterion,Image processing | Autoregressive model,Data set,Pattern recognition,Expectation–maximization algorithm,Image processing,Parametric statistics,Dynamic models,Artificial intelligence,Hidden Markov model,Machine learning,Mathematics,Hidden semi-Markov model | Journal |
Volume | Issue | ISSN |
37 | 9 | 0031-3203 |
Citations | PageRank | References |
7 | 0.61 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gilles Celeux | 1 | 842 | 112.78 |
| Jacinto C. Nascimento | 2 | 36 | 2.47 |
| Jorge Marques | 3 | 22 | 1.90 |