Using Dempster's rule of combination to robustly estimate pointed targets

摘要

In this paper we address an important issue in human-robot interaction, that of accurately deriving pointing information from a corresponding gesture. Based on the fact that in most applications it is the pointed object rather than the actual pointing direction which is important, we formulate a novel approach which takes into account prior information about the location of possible pointed targets. To decide about the pointed object, the proposed approach uses the Dempster-Shafer theory of evidence to fuse information from two different input streams: head pose, estimated by visually tracking the off-plane rotations of the face, and hand pointing orientation. Detailed experimental results are presented that validate the effectiveness of the method in realistic application setups.