Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
International Journal of Computer Vision
A Low Dimensional Fluid Motion Estimator
International Journal of Computer Vision
A variational framework for spatio-temporal smoothing of fluid motions
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Adaptive particle filter based on energy field for robust object tracking in complex scenes
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
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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 Itô 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.