Parameterized Models for Optical Flow

Research Overview

Techniques for estimating motion from video image sequences have improved significantly in the last ten years. One key to the success of many techniques is the use of parameterized models of motion that help constrain the estimation problem. It has been common, for example, to assume that motion between frames of an image sequence is well described as an affine deformation. Although these models work well with smooth surfaces and fairly simple rigid motions, there are many other types of complex motions for which they are less effective.

We have been exploring the design and use of parameterized motion models that represent much more varied and complex motions. Our goals are threefold: to construct simple, linear models of complex motion phenomena; to estimate the coefficients of these linear models directly from spatiotemporal image derivatives; and to recognize or classify motion ``events'' from the recovered coefficients. We are interested in three types of complex motions classes:

Related Publications

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