2.5D Visual Tracking and Layered Image Analysis
Research Overview
There exists a theoretical gap between model-based tracking of complex
objects and early motion estimation, namely, the selection and initialization
of models. It seems clear that some form of early analysis should help
one select and initialize models, but this remains a largely unexplored
topic. Allan Jepson, Michael Black and I have been working on methods for
extracting effective representations of visual motion that provide an
efficient characterization of the principal moving components of a scene
and their relative depths. In future research we hope to use this
representation to infer the occurrence of plausible models for subsequent
model selection and refinement.
To provide stable image descriptions through time, Allan Jepson, Thomas
El-Maraghi (University of Toronto) and I have developed an approach
to learning 2D models of image appearance. The approach works with an
online version of the EM algorithm to identify stable image structure
during tracking. In this way the most stable regions can play the most
significant role in the tracking, to facilitate tracking over long image
sequences with precise image alignment. This work was awarded Runner-Up
for the Best Paper at the IEEE Conference on Compuer Vision and Pattern
Recognition in 2001.
Related Publications
- Jepson, A.D., Fleet, D.J. and El-Maraghi, T. (2001) Robust,
on-line appearance models for vision tracking.
IEEE Conference on Computer Vision and Pattern Recognition,
Kauai, Vol. I, pp. 415--422
[Runner-Up for best paper]
(compressed pdf)
© IEEE
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