Jepson, A.D., Fleet, D.J. and El-Maraghi, T.
Robust online appearance models for visual tracking.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
We propose a framework for learning robust, adaptive, appearance models
to be used for motion-based tracking of natural objects. The model adapts
to slowly changing appearance, and it maintains a natural measure of
the stability of the observed image structure during tracking.
By identifying stable properties of appearance we can weight them more
heavily for motion estimation, while less stable properties can be
proportionately downweighted. The approach involves a mixture of
stable image structure, learned over long time courses, along with
2-frame motion information and an outlier process. An on-line
EM-algorithm is used to adapt the appearance model parameters over time.
An implementation of this approach is developed for an appearance model
based on the filter responses from a steerable pyramid. This model is
used in a motion-based tracking algorithm to provide robustness in the
face of image outliers, such as those caused by occlusions, while adapting
to natural changes in appearance such as those due to facial expressions
or variations in 3D pose.
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