  In this paper, we present an approach for identification of actions within
depth action videos. First, we process the video to get motion history images
(MHIs) and static history images (SHIs) corresponding to an action video based
on the use of 3D Motion Trail Model (3DMTM). We then characterize the action
video by extracting the Gradient Local Auto-Correlations (GLAC) features from
the SHIs and the MHIs. The two sets of features i.e., GLAC features from MHIs
and GLAC features from SHIs are concatenated to obtain a representation vector
for action. Finally, we perform the classification on all the action samples by
using the l2-regularized Collaborative Representation Classifier (l2-CRC) to
recognize different human actions in an effective way. We perform evaluation of
the proposed method on three action datasets, MSR-Action3D, DHA and UTD-MHAD.
Through experimental results, we observe that the proposed method performs
superior to other approaches.
