  The computer vision community is currently focusing on solving action
recognition problems in real videos, which contain thousands of samples with
many challenges. In this process, Deep Convolutional Neural Networks (D-CNNs)
have played a significant role in advancing the state-of-the-art in various
vision-based action recognition systems. Recently, the introduction of residual
connections in conjunction with a more traditional CNN model in a single
architecture called Residual Network (ResNet) has shown impressive performance
and great potential for image recognition tasks. In this paper, we investigate
and apply deep ResNets for human action recognition using skeletal data
provided by depth sensors. Firstly, the 3D coordinates of the human body joints
carried in skeleton sequences are transformed into image-based representations
and stored as RGB images. These color images are able to capture the
spatial-temporal evolutions of 3D motions from skeleton sequences and can be
efficiently learned by D-CNNs. We then propose a novel deep learning
architecture based on ResNets to learn features from obtained color-based
representations and classify them into action classes. The proposed method is
evaluated on three challenging benchmark datasets including MSR Action 3D,
KARD, and NTU-RGB+D datasets. Experimental results demonstrate that our method
achieves state-of-the-art performance for all these benchmarks whilst requiring
less computation resource. In particular, the proposed method surpasses
previous approaches by a significant margin of 3.4% on MSR Action 3D dataset,
0.67% on KARD dataset, and 2.5% on NTU-RGB+D dataset.
