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, 25(10):1296-1311, 2003

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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