title: A stochastic filtering technique for fluid flow velocity fields tracking.
abstract: 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.
authors.affiliation: European University of Brittany-UBS, CNRS UMR 3192-Lab-STICC, BP 573, F-56000 Vannes Cedex, France. anne.cuzol@univ-ubs.fr
authors.forename: Anne
authors.initials: A
authors.lastname: Cuzol
authors.forename: Etienne
authors.initials: E
authors.lastname: Mémin
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D015233:Models, Statistical; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012212:Rheology / Q000379:methods*; D012680:Sensitivity and Specificity; D013269:Stochastic Processes; D013382:Subtraction Technique