Distribution of Action Movements (DAM): A Descriptor for Human Action
  Recognition
Facundo Manuel Quiroga Franco Ronchetti Laura Lanzarini Cesar Eestrebou
Human action recognition from skeletal data is an important and active area
of research in which the state of the art has not yet achieved near-perfect
accuracy on many well-known datasets. In this paper, we introduce the
Distribution of Action Movements Descriptor, a novel action descriptor based on
the distribution of the directions of the motions of the joints between frames,
over the set of all possible motions in the dataset. The descriptor is computed
as a normalized histogram over a set of representative directions of the
joints, which are in turn obtained via clustering. While the descriptor is
global in the sense that it represents the overall distribution of movement
directions of an action, it is able to partially retain its temporal structure
by applying a windowing scheme.
  The descriptor, together with a standard classifier, outperforms several
state-of-the-art techniques on many well-known datasets.