Uncertainty in Artificial Intelligence
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Lattice Particle Filters
Dirk Ormoneit, Christiane Lemieux, David Fleet
A standard approach to approximate inference in state-space models isto apply a particle filter, e.g., the Condensation Algorithm.However, the performance of particle filters often varies significantlydue to their stochastic nature.We present a class of algorithms, called lattice particle filters, thatcircumvent this difficulty by placing the particles deterministicallyaccording to a Quasi-Monte Carlo integration rule.We describe a practical realization of this idea, discuss itstheoretical properties, and its efficiency.Experimental results with a synthetic 2D tracking problem show that thelattice particle filter is equivalent to a conventional particle filterthat has between 10 and 60% more particles, depending ontheir ``sparsity'' in the state-space.We also present results on inferring 3D human motion frommoving light displays.
Pages: 395-402
PS Link:
PDF Link: /papers/01/p395-ormoneit.pdf
AUTHOR = "Dirk Ormoneit and Christiane Lemieux and David Fleet",
TITLE = "Lattice Particle Filters",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "395--402"

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