A Stochastic Filter for Fluid Motion Tracking

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A Stochastic Filter for Fluid Motion Tracking
In this paper we present a method for the tracking of fluid flows velocity fields. The technique we propose is formalized within sequential Bayesian filter framework. The filter we propose here combines an It^o diffusion process coming from a stochastic formulation of the vorticity-velocity form of Navier-Stokes equation and discrete measurements extracted from an image sequence. The resulting tracker provides robust and consistent estimations of instantaneous motion fields along the whole image sequence. In order to handle a state space of reasonable dimension for the stochastic filtering problem, we represent the motion field as a combination of adapted basis functions. The used basis functions ensue from a mollification of Biot-Savart integral and a discretization of the vorticity and divergence maps of the fluid vector field. The efficiency of the method is demonstrated on a long real world sequence showing a vortex launch at tip of airplane wing.
Anne Cuzol, Étienne Mémin
Added 15 Oct 2009
Updated 30 Oct 2009
Type Conference
Year 2005
Where ICCV
Authors Anne Cuzol, Étienne Mémin
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