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|a eng
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| 100 |
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|a Veeraraghavan, Ashok
|e verfasserin
|4 aut
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| 245 |
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|a Matching shape sequences in video with applications in human movement analysis
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|c 2005
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
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|2 rdamedia
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|a Band
|b nc
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|a Date Completed 19.01.2006
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|a Date Revised 15.11.2006
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|a published: Print
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|a Citation Status MEDLINE
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|a We present an approach for comparing two sequences of deforming shapes using both parametric models and nonparametric methods. In our approach, Kendall's definition of shape is used for feature extraction. Since the shape feature rests on a non-Euclidean manifold, we propose parametric models like the autoregressive model and autoregressive moving average model on the tangent space and demonstrate the ability of these models to capture the nature of shape deformations using experiments on gait-based human recognition. The nonparametric model is based on Dynamic Time-Warping. We suggest a modification of the Dynamic time-warping algorithm to include the nature of the non-Euclidean space in which the shape deformations take place. We also show the efficacy of this algorithm by its application to gait-based human recognition. We exploit the shape deformations of a person's silhouette as a discriminating feature and provide recognition results using the nonparametric model. Our analysis leads to some interesting observations on the role of shape and kinematics in automated gait-based person authentication
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|a Comparative Study
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|a Journal Article
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|a Research Support, U.S. Gov't, Non-P.H.S.
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|a Roy-Chowdhury, Amit K
|e verfasserin
|4 aut
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| 700 |
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|a Chellappa, Rama
|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 27(2005), 12 vom: 19. Dez., Seite 1896-909
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|g pages:1896-909
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