Authors:
Yann Lepoittevin
1
;
Isabelle Herlin
1
and
Dominique Béréziat
2
Affiliations:
1
Inria Paris-Rocquencourt, France
;
2
UPMC Univ Paris 06, CNRS and UMR 7606, France
Keyword(s):
Data Assimilation, Ensemble Kalman Filter, Localization, Motion Estimation, Optical Flow.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Optical Flow and Motion Analyses
Abstract:
This paper designs an Image-based Ensemble Kalman Filter (IEnKF), whose components are defined only
from image properties, to estimate motion on image sequences. The key elements of this filter are, first,
the construction of the initial ensemble, and second, the propagation in time of this ensemble on the studied
temporal interval. Both are analyzed in the paper and their impact on results is discussed with synthetic and real
data experiments. The initial ensemble is obtained by adding a Gaussian vector field to an estimate of motion
on the first two frames. The standard deviation of this normal law is computed from motion results given by
a set of optical flow methods of the literature. It describes the uncertainty on the motion value at initial date.
The propagation in time of the ensemble members relies on the following evolution laws: transport by velocity
of the image brightness function and Euler equations for the motion function. Shrinking of the ensemble is
avoided thanks to
a localization method and the use of observation ensembles, both techniques being defined
from image characteristics. This Image-based Ensemble Kalman Filter is quantified on synthetic experiments
and applied on traffic and meteorological images.
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