Bayesian Analysis of Image Sequences:
Detection and Tracking of Motion Boundaries
David Fleet
Xerox PARC and Queen's University
Abstract
Motion analysis concerns the estimation and recognition of motion from
image sequences. It is useful for scene segmentation, estimating 3D
surface structure and camera motion, and for the detection and tracking
of objects, such as people. A long-standing problem in motion analysis
has been the detection and estimation of motion in the neighborhoods of
surface boundaries, where motion in the image is discontinuous and
occlusions cause image structure to appear or disappear from one image
to the next. Although these "motion boundaries" are often viewed as
a source of noise for current motion estimation techniques, we can also
view them as a rich source of information about the location of surface
boundaries and the depth ordering of surfaces at these locations.
We propose a Bayesian framework for representing and estimating image
motion in terms of multiple motion models, including both smooth motion
and local motion discontinuity models. We compute the posterior
probability distribution over models and model parameters, given the
image data, using discrete samples and a particle filter for propagating
beliefs through time. In this talk I will introduce the problem then
describe our Bayesian framework, including the generative models, the
likelihood computation, the particle filter, and a mixture model prior
from which samples are drawn. I will also present some recent
experimental results.
About the Speaker
David Fleet is a research scientist at the Xerox Palo Alto Research
Center (PARC), and an Associate Professor in the Department of
Computer Science at Queen's University, Kingston, Canada. After
receiving a PhD in Computer Science from the University of Toronto in
1991, Dr. Fleet joined the Department of Computer Science at Queen's
University, with cross-appointments to the Departments of Psychology,
and Electrical Engineering. In 1999 he joined the Digital Video
Analysis Group at Xerox PARC, where his research is focused on motion
analysis in computer vision. Dr. Fleet was awarded an Alfred P. Sloan
Research Fellowship for his research on biological vision. In 1999 his
paper with Michael Black on probabilistic detection and tracking of
motion discontinuities received Honorable Mention for the Marr Prize
at the International Conference on Computer Vision. His research
interests include computer vision, image processing, visual perception,
and visual neuroscience. He has published research articles and one
book on various topics including the estimation of optical flow and
stereoscopic disparity, probabilistic methods in motion analysis,
modeling appearance changes in image sequences, non-Fourier motion
and stereo perception, and the neural basis of stereo vision.
bac-coordinators@cs.stanford.edu
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Last modified:
Tue Sep 21 18:59:46 PDT 1999