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Most algorithms for tracking objects in video consist of two components: a model of the dynamics of the object being tracked, and a model of its appearance. Often the appearance model is constructed before tracking, perhaps from training images, and then used as-is when tracking test sequences.
What if the test sequence contains appearances of the object, or lighting conditions, that don't exactly match those of the training data? Typically, trackers with fixed appearance models will perform poorly under these circumstances.
In this project we make use of the new appearance information that comes available during tracking to incrementally improve a subspace appearance model of the target. The key to this algorithm is a novel incremental
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