(Full text is available in Postscript
).
Multi-Frame Optical Flow Estimation Using Subspace Constraint
Michal Irani.
We show that the set of all flow-fields in a sequence of frames imaging
a rigid scene resides in a low-dimensional linear subspace. Based on this
observation, we develop a method for simultaneous estimation of optical-flow
across multiple frames, which uses these subspace constraints. The multi-frame
subspace constraints are strong constraints, and replace commonly used
heuristic constraints, such as spatial or temporal smoothness. The
subspace constraints are geometrically meaningful, and are not
violated at depth discontinuities, or when the camera-motion changes abruptly.
Furthermore, we show that the subspace constraints on flow-fields
apply for a variety of imaging models, scene models, and motion
models. Hence, the presented approach for constrained multi-frame flow
estimation
is general. However, our approach does not require
prior knowledge of the underlying world or camera model.
Although linear subspace constraints have been used successfully in
the past for recovering 3D information (e.g., [Tomasi&Kanade]), it
has been assumed that 2D correspondences are given. However, correspondence
estimation is a fundamental problem in motion analysis. In this paper,
we use multi-frame subspace constraints to constrain the 2D
correspondence estimation process itself, and not for
3D recovery.