A dynamic Bayesian network approach to figure tracking using learned dynamic models

A dynamic Bayesian network approach to figure tracking using learned dynamic models (1999)

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by Vladimir Pavlović , James M. Rehg , Tat-jen Cham , Kevin P. Murphy

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@INPROCEEDINGS{Pavlović99adynamic,
    author = {Vladimir Pavlović and James M. Rehg and Tat-jen Cham and Kevin P. Murphy},
    title = {A dynamic Bayesian network approach to figure tracking using learned dynamic models},
    booktitle = {},
    year = {1999},
    pages = {94--101}
}

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Abstract

The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. However, most work on tracking and synthesizing figure motion has employed either simple, generic dynamic models or highly specific hand-tailored ones. Recently, a broad class of learning and inference algorithms for time-series models have been successfully cast in the framework of dynamic Bayesian networks (DBNs). This paper describes a novel DBN-based switching linear dynamic system (SLDS) model and presents its application to figure motion analysis. A key feature of our approach is an approximate Viterbi inference technique for overcoming the intractability of exact inference in mixed-state DBNs. We present experimental results for learning figure dynamics from video data and show promising initial results for tracking, interpolation, synthesis, and classification using learned models. 1

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