This paper presents a novel view-independent
approach to the recognition of human gestures of several
people in low resolution sequences from multiple calibrated
cameras. In contraposition with other multi-ocular gesture
recognition systems based on generating a classification on
a fusion of features coming from different views, our system
performs a data fusion (3D representation of the scene) and
then a feature extraction and classification. Motion descriptors
introduced by Bobick et al. for 2D data are extended
to 3D and a set of features based on 3D invariant statistical
moments are computed. Finally, a Bayesian classifier is employed
to perform recognition over a small set of actions. Results
are provided showing the effectiveness of the proposed
algorithm in a SmartRoom scenario.Peer ReviewedPostprint (published version