Title:
Learning General Optical Flow Subspaces for Egomotion Estimation and Detection of Motion Anomalies
Learning General Optical Flow Subspaces for Egomotion Estimation and Detection of Motion Anomalies
Author(s)
Roberts, Richard
Potthast, Christian
Dellaert, Frank
Potthast, Christian
Dellaert, Frank
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Abstract
This paper deals with estimation of dense optical flow
and ego-motion in a generalized imaging system by exploiting
probabilistic linear subspace constraints on the flow.
We deal with the extended motion of the imaging system
through an environment that we assume to have some degree
of statistical regularity. For example, in autonomous
ground vehicles the structure of the environment around the
vehicle is far from arbitrary, and the depth at each pixel
is often approximately constant. The subspace constraints
hold not only for perspective cameras, but in fact for a
very general class of imaging systems, including catadioptric
and multiple-view systems. Using minimal assumptions
about the imaging system, we learn a probabilistic
subspace constraint that captures the statistical regularity
of the scene geometry relative to an imaging system. We
propose an extension to probabilistic PCA (Tipping and
Bishop, 1999) as a way to robustly learn this subspace
from recorded imagery, and demonstrate its use in conjunction
with a sparse optical flow algorithm. To deal with the
sparseness of the input flow, we use a generative model to
estimate the subspace using only the observed flow measurements.
Additionally, to identify and cope with image regions
that violate subspace constraints, such as moving objects,
objects that violate the depth regularity, or gross flow
estimation errors, we employ a per-pixel Gaussian mixture
outlier process. We demonstrate results of finding the optical
flow subspaces and employing them to estimate dense
flow and to recover camera motion for a variety of imaging
systems in several different environments.
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Date Issued
2009
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