Probabilistic Tracking with Exemplars in a Metric Space

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Probabilistic Tracking with Exemplars in a Metric Space
Abstract. A new, exemplar-based, probabilistic paradigm for visual tracking is presented. Probabilistic mechanisms are attractive because they handle fusion of information, especially 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" (M2 ) approach, which has a number of attractions. Principally, it provides alternatives to standard learning algorithms by allowing the use of metrics that are not embedded in a vector space. Secondly, 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 effectiven...
Kentaro Toyama, Andrew Blake
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 2002
Where IJCV
Authors Kentaro Toyama, Andrew Blake
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