  Automatic human action recognition is indispensable for almost artificial
intelligent systems such as video surveillance, human-computer interfaces,
video retrieval, etc. Despite a lot of progress, recognizing actions in an
unknown video is still a challenging task in computer vision. Recently, deep
learning algorithms have proved its great potential in many vision-related
recognition tasks. In this paper, we propose the use of Deep Residual Neural
Networks (ResNets) to learn and recognize human action from skeleton data
provided by Kinect sensor. Firstly, the body joint coordinates are transformed
into 3D-arrays and saved in RGB images space. Five different deep learning
models based on ResNet have been designed to extract image features and
classify them into classes. Experiments are conducted on two public video
datasets for human action recognition containing various challenges. The
results show that our method achieves the state-of-the-art performance
comparing with existing approaches.
