  Human actions recognition is a fundamental task in artificial vision, that
has earned a great importance in recent years due to its multiple applications
in different areas. %, such as the study of human behavior, security or video
surveillance. In this context, this paper describes an approach for real-time
human action recognition from raw depth image-sequences, provided by an RGB-D
camera. The proposal is based on a 3D fully convolutional neural network, named
3DFCNN, which automatically encodes spatio-temporal patterns from depth
sequences without %any costly pre-processing. Furthermore, the described 3D-CNN
allows %automatic features extraction and actions classification from the
spatial and temporal encoded information of depth sequences. The use of depth
data ensures that action recognition is carried out protecting people's
privacy% allows recognizing the actions carried out by people, protecting their
privacy%\sout{of them} , since their identities can not be recognized from
these data. %\st{ from depth images.} 3DFCNN has been evaluated and its results
compared to those from other state-of-the-art methods within three widely used
%large-scale NTU RGB+D datasets, with different characteristics (resolution,
sensor type, number of views, camera location, etc.). The obtained results
allows validating the proposal, concluding that it outperforms several
state-of-the-art approaches based on classical computer vision techniques.
Furthermore, it achieves action recognition accuracy comparable to deep
learning based state-of-the-art methods with a lower computational cost, which
allows its use in real-time applications.
