Invariant action classification with volumetric data

We propose an action recognition algorithm in which the image sequences capturing a moving human body produced by a significant number of cameras are first used to generate a volumetric representation of the body by means of volumetric intersection. Classification is then performed directly on 3D data, making the system inherently insensitive to viewpoint dependence and motion trajectory variability. Suitable features are extracted from the voxset approximating the body, and fed to a hidden Markov model to produce a finite-state description of the motion. The Kullback-Leibler distance is finally used to classify new sequences.