People Tracking Using Hybrid Monte Carlo Filtering

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People Tracking Using Hybrid Monte Carlo Filtering
Particle filters are used for hidden state estimation with nonlinear dynamical systems. The inference of 3-d human motion is a natural application, given the nonlinear dynamics of the body and the nonlinear relation between states and image observations. However, the application of particle filters has been limited to cases where the number of state variables is relatively small, because the number of samples needed with high dimensional problems can be prohibitive. We describe a filter that uses hybrid Monte Carlo (HMC) to obtain samples in high dimensional spaces. It uses multiple Markov chains that use posterior gradients to rapidly explore the state space, yielding fair samples from the posterior. We find that the HMC filter is several thousand times faster than a conventional particle filter on a 28D people tracking problem.
Kiam Choo, David J. Fleet
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2001
Where ICCV
Authors Kiam Choo, David J. Fleet
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