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                 Robust online appearance models for visual tracking

                                                 David Fleet
                                 Xerox Palo Alto Research Center

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We propose a framework for learning robust, adaptive, appearance models
to be used for motion-based tracking of natural objects. 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. It is also provides the ability to adapt to natural
changes in appearance, such as those due to facial expressions or
variations in 3D pose. We show experimental results on a variety of
natural image sequences of people moving within cluttered environments.

Joint work with Allan Jepson and Thomas El-Maraghi.