Learning switching dynamic models for objects tracking

DOI10.1016/j.patcog.2004.01.020zbMath1070.68644OpenAlexW2162602424MaRDI QIDQ1886686

Gilles Celeux, J. C. Nascimento, Jorge S. Marques

Publication date: 18 November 2004

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://hal.inria.fr/inria-00071720/file/RR-4863.pdf

zbMATH Keywords

EM algorithmImage processingBIC criterionHidden Markov chainAuto regressive model

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10) Machine vision and scene understanding (68T45)

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Cites Work

• Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
• Tools for statistical inference. Observed data and data augmentation methods
• Estimating the dimension of a model
• Optimization of mixture models: Comparison of different strategies
• Improving the robustness of parametric shape tracking with switched multiple models
• Likelihood ratio inequalities with applications to various mixtures
• Practical Bayesian Density Estimation Using Mixtures of Normals
• Bayes Factors
• A new look at the statistical model identification
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