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Topology free hidden Markov models: application to background modeling

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5 Author(s)
B. Stenger ; Dept. of Eng., Cambridge Univ., UK ; V. Ramesh ; N. Paragios ; F. Coetzee
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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

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Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on  (Volume:1 )

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