Topology Free Hidden Markov Models: Application to Background Modeling
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, real-world 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.
Cite
Text
Stenger et al. "Topology Free Hidden Markov Models: Application to Background Modeling." IEEE/CVF International Conference on Computer Vision, 2001. doi:10.1109/ICCV.2001.10008Markdown
[Stenger et al. "Topology Free Hidden Markov Models: Application to Background Modeling." IEEE/CVF International Conference on Computer Vision, 2001.](https://mlanthology.org/iccv/2001/stenger2001iccv-topology/) doi:10.1109/ICCV.2001.10008BibTeX
@inproceedings{stenger2001iccv-topology,
title = {{Topology Free Hidden Markov Models: Application to Background Modeling}},
author = {Stenger, Bjoern and Ramesh, Visvanathan and Paragios, Nikos and Coetzee, Frans and Buhmann, Joachim M.},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2001},
pages = {294-301},
doi = {10.1109/ICCV.2001.10008},
url = {https://mlanthology.org/iccv/2001/stenger2001iccv-topology/}
}