Evidential modeling for pose estimation

Abstract : Pose estimation involves reconstructing the configura- tion of a moving body from images sequences. In this paper we present a general framework for pose esti- mation of unknown objects based on Shafer's eviden- tial reasoning. During learning an evidential model of the object is built, integrating different image fea- tures to improve both estimation robustness and pre- cision. All the measurements coming from one or more views are expressed as belief functions, and com- bined through Dempster's rule. The best pose esti- mate at each time step is then extracted from the resulting belief function by probabilistic approxima- tion. The choice of a sufficiently dense training set is a critical problem. Experimental results concerning a human tracking system are shown.
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Communication dans un congrès
4th International Symposium on Imprecise Probabilities and Their Applications (ISIPTA'05), Jul 2005, Pittsburgh, United States. 2005
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Soumis le : vendredi 6 mai 2011 - 14:50:03
Dernière modification le : jeudi 14 juin 2018 - 10:54:02
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  • HAL Id : inria-00590191, version 1

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Fabio Cuzzolin, Ruggero Frezza. Evidential modeling for pose estimation. 4th International Symposium on Imprecise Probabilities and Their Applications (ISIPTA'05), Jul 2005, Pittsburgh, United States. 2005. 〈inria-00590191〉

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