  Video-based human action recognition is currently one of the most active
research areas in computer vision. Various research studies indicate that the
performance of action recognition is highly dependent on the type of features
being extracted and how the actions are represented. Since the release of the
Kinect camera, a large number of Kinect-based human action recognition
techniques have been proposed in the literature. However, there still does not
exist a thorough comparison of these Kinect-based techniques under the grouping
of feature types, such as handcrafted versus deep learning features and
depth-based versus skeleton-based features. In this paper, we analyze and
compare ten recent Kinect-based algorithms for both cross-subject action
recognition and cross-view action recognition using six benchmark datasets. In
addition, we have implemented and improved some of these techniques and
included their variants in the comparison. Our experiments show that the
majority of methods perform better on cross-subject action recognition than
cross-view action recognition, that skeleton-based features are more robust for
cross-view recognition than depth-based features, and that deep learning
features are suitable for large datasets.
