  In the last years, the computer vision research community has studied on how
to model temporal dynamics in videos to employ 3D human action recognition. To
that end, two main baseline approaches have been researched: (i) Recurrent
Neural Networks (RNNs) with Long-Short Term Memory (LSTM); and (ii) skeleton
image representations used as input to a Convolutional Neural Network (CNN).
Although RNN approaches present excellent results, such methods lack the
ability to efficiently learn the spatial relations between the skeleton joints.
On the other hand, the representations used to feed CNN approaches present the
advantage of having the natural ability of learning structural information from
2D arrays (i.e., they learn spatial relations from the skeleton joints). To
further improve such representations, we introduce the Tree Structure Reference
Joints Image (TSRJI), a novel skeleton image representation to be used as input
to CNNs. The proposed representation has the advantage of combining the use of
reference joints and a tree structure skeleton. While the former incorporates
different spatial relationships between the joints, the latter preserves
important spatial relations by traversing a skeleton tree with a depth-first
order algorithm. Experimental results demonstrate the effectiveness of the
proposed representation for 3D action recognition on two datasets achieving
state-of-the-art results on the recent NTU RGB+D~120 dataset.

Computer Vision and Pattern Recognition;Machine Learning