Issue No. 07 - July (2009 vol. 31)
Anne Cuzol , European University of Brittany-UBS, CNRS, UMR, France
Etienne Mémin , Centre INRIA Rennes-Bretagne Atlantique, Campus Universitaire de Beaulieu, France
In this paper, we present a method for the temporal tracking of fluid flow velocity fields. The technique we propose is formalized within a sequential Bayesian filtering framework. The filtering model combines an Itô diffusion process coming from a stochastic formulation of the vorticity-velocity form of the Navier-Stokes equation and discrete measurements extracted from the image sequence. In order to handle a state space of reasonable dimension, the motion field is represented as a combination of adapted basis functions, derived from a discretization of the vorticity map of the fluid flow velocity field. The resulting nonlinear filtering problem is solved with the particle filter algorithm in continuous time. An adaptive dimensional reduction method is applied to the filtering technique, relying on dynamical systems theory. The efficiency of the tracking method is demonstrated on synthetic and real-world sequences.
Motion estimation, tracking, nonlinear stochastic filtering, fluid flows.
E. Mémin and A. Cuzol, "A Stochastic Filtering Technique for Fluid Flow Velocity Fields Tracking," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 31, no. , pp. 1278-1293, 2008.