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In this paper, we describe a novel variational Monte Carlo approach for modeling and tracking
body parts of articulated objects. An articulated object (human target) is represented as a
dynamic Markov network of the different constituent parts. The proposed approach combines
local information of individual body parts and other spatial constraints influenced by
neighboring parts. The movement of the relative parts of the articulated body is modeled with
local information of displacements from the Markov network and the global information from
other neighboring parts. We explore the effect of certain model parameters (including the
number of parts tracked; number of MonteCarlo cycles, etc.) on system accuracy and show that
ourvariational Monte Carlo approach achieves better efficiency and effectiveness compared to
other methods on a number of realtime video datasets containing single targets.
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