Topology Free Hidden Markov Models: Application to Background Modeling

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.

• Type: Conference
• Year: 2001
• Where: ICCV
• Authors: Bjoern Stenger, Visvanathan Ramesh, Nikos Paragios, Frans Coetzee, Joachim M. Buhmann
• Keywords: Background Modeling Problem, Computer Vision, HMM Topology, ICCV 2001, Model Selection Problem, Model Topology, Parameter Estimation