FOURTH INTERNATIONAL SYMPOSIUM ON
IMPRECISE PROBABILITIES AND THEIR APPLICATIONS
Carnegie Mellon University
Pittsburgh, PA, USA
July 20-23 2005

ISIPTA'05 ELECTRONIC PROCEEDINGS

Fabio Cuzzolin, Ruggero Frezza

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.

Keywords. Pose estimation, training set, feature-pose maps, belief functions, evidential model

Paper Download

The paper is availabe in the following formats:

Presentation files

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


[ back to the Proceedings of ISIPTA'05 home page 
Send any remarks to the following address: smc@decsai.ugr.es