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.
Topology free hidden Markov models: application to background modeling
Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 ; 1 ; 294-301 vol.1
2001-01-01
805662 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Topology Free Hidden Markov Models: Application to Background Modeling
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