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231224s2013 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2012.263
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|a eng
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| 100 |
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|a Tosato, Diego
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Characterizing humans on Riemannian manifolds
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|c 2013
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|a Text
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|a ƒaComputermedien
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|a Date Completed 17.02.2014
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|a Date Revised 21.06.2013
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|a published: Print
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|a Citation Status MEDLINE
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|a In surveillance applications, head and body orientation of people is of primary importance for assessing many behavioral traits. Unfortunately, in this context people are often encoded by a few, noisy pixels so that their characterization is difficult. We face this issue, proposing a computational framework which is based on an expressive descriptor, the covariance of features. Covariances have been employed for pedestrian detection purposes, actually a binary classification problem on Riemannian manifolds. In this paper, we show how to extend to the multiclassification case, presenting a novel descriptor, named weighted array of covariances, especially suited for dealing with tiny image representations. The extension requires a novel differential geometry approach in which covariances are projected on a unique tangent space where standard machine learning techniques can be applied. In particular, we adopt the Campbell-Baker-Hausdorff expansion as a means to approximate on the tangent space the genuine (geodesic) distances on the manifold in a very efficient way. We test our methodology on multiple benchmark datasets, and also propose new testing sets, getting convincing results in all the cases
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|a Journal Article
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|a Spera, Mauro
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cristani, Marco
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Murino, Vittorio
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 35(2013), 8 vom: 20. Aug., Seite 1972-84
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|u http://dx.doi.org/10.1109/TPAMI.2012.263
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