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.
Keywords. Pose estimation, training set, feature-pose maps, belief functions, evidential model
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Authors addresses:
Fabio Cuzzolin
Computer Science Department
University of California, Los Angeles
3811A Boelter Hall
Los Angeles, CA 90095-1596
Ruggero Frezza
Dipartimento di Elettronica e Informatica
Via Ognissanti 72
35131 Padova
Italy
E-mail addresses:
| Fabio Cuzzolin | cuzzolin@cs.ucla.edu |
| Ruggero Frezza | frezza@dei.unipd.it |