Evidential modeling for pose estimation

Pose estimation involves reconstructing the configuration of a moving body from images sequences. In this paper we present a general framework for pose estimation of unknown objects based on Shafer’s evidential reasoning. During learning an evidential model of the object is built, integrating different image features to improve both estimation robustness and precision. All the measurements coming from one or more views are expressed as belief functions, and combined through Dempster’s rule. The best pose estimate at each time step is then extracted from the resulting belief function by probabilistic approximation. 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, featurepose maps, belief functions, evidential model.

• Type: Conference
• Year: 2005
• Where: ISIPTA
• Authors: Fabio Cuzzolin, Ruggero Frezza
• Keywords: Belief Functions, Evidential Model, ISIPTA 2005, Mathematics, Pose Estimation