Incremental Learning for Robust Visual Tracking
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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