Improving action classification with volumetric data using 3D morphological operators

This work deals with the definition of a framework for interpreting, modeling and classifying sequences of body movements into a pre-defined vocabulary of actions. Starting from sequences of volumetric reconstructions of the actor pose in each frame, we split action recognition into three separated tasks. The first task is the representation of the four-dimensional patterns reconstructed from each sequence, the second task is the extraction of motion descriptors, and the third task is the classification into action classes. In particular, we extract the curve skeleton from the reconstructed volumes in order to underly the actor movements and to reduce the system dependence from the actor gender and the body shape. The proposed method increases the action recognition rate.