Probabilistic tracking in a metric space

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Abstract
A new, exemplar-based, probabilistic paradigm for vi- sual tracking is presented. Probabilistic mechanisms are attractive because they handle fusion of information, espe- cially temporal fusion, in a principled manner. Exemplars are selected representatives of raw training data, used here to represent probabilistic mixture distributions of object configurations. Their use avoids tedious hand-construction of object models and problems with changes of topology. Using exemplars in place of a parameterized model poses several challenges, addressed here with what we call the "Metric Mixture" (M ) approach. The M model has several valuable properties. Principally, it provides alter- natives to standard learning algorithms by allowing the use of metrics that are not embedded in a vector space. Sec- ondly, it uses a noise model that is learned from training data. Lastly, it eliminates any need for an assumption of probabilistic pixelwise independence. Experiments demonstrate the effectiveness of the M model in two domains: tracking walking people using chamfer distances on binary edge images and tracking mouth movements by means of a shuffle distance.

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