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 28 D people tracking problem.
People tracking using hybrid Monte Carlo filtering
Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 ; 2 ; 321-328 vol.2
2001-01-01
807368 byte
Conference paper
Electronic Resource
English
People Tracking Using Hybrid Monte Carlo Filtering
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