Topology Free Hidden Markov Models: Application to Background Modeling (2001)
| Venue: | In IEEE International Conference on Computer Vision |
| Citations: | 38 - 2 self |
BibTeX
@INPROCEEDINGS{Stenger01topologyfree,
author = {B. Stenger and V. Ramesh and N. Paragios and F.Coetzee and J.M. Buhmann},
title = {Topology Free Hidden Markov Models: Application to Background Modeling},
booktitle = {In IEEE International Conference on Computer Vision},
year = {2001},
pages = {294--301}
}
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Abstract
Hidden Markov Models (HMMs) are increasingly being used in computer vision for applications such as: gesture analysis, action recognition from video, and illumination modeling. Their use involves an off-line learning step that is used as a basis for on-line decision making (i.e. a stationarity assumption on the model parameters). But, realworld applications are often non-stationary in nature. This leads to the need for a dynamic mechanism to learn and update the model topology as well as its parameters. This paper presents a new framework for HMM topology and parameter estimation in an online, dynamic fashion. The topology and parameter estimation is posed as a model selection problem with an MDL prior. Online modifications to the topology are made possible by incorporating a state splitting criterion. To demonstrate the potential of the algorithm, the background modeling problem is considered. Theoretical validation and real experiments are presented. 1
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