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Title:Probabilistic Tracking with Exemplars in a Metric Space
Authors: K Toyama ; Andrew Blake
Date: 2002
Publication Title:International Journal of Computer Vision
Publication Type:Journal Article Publication Status:Published
Volume No:48 (1) Page Nos:9-19
DOI:10.1023/A:1014899027014 ISBN/ISSN:1573-1405
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" (M^2) 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 effectiveness of the M^2 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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Bibtex format
author = { K Toyama and Andrew Blake },
title = {Probabilistic Tracking with Exemplars in a Metric Space},
journal = {International Journal of Computer Vision},
publisher = {Springer},
year = 2002,
volume = {48 (1)},
pages = {9-19},
doi = {10.1023/A:1014899027014},

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