  Deep learning techniques are being used in skeleton based action recognition
tasks and outstanding performance has been reported. Compared with RNN based
methods which tend to overemphasize temporal information, CNN-based approaches
can jointly capture spatio-temporal information from texture color images
encoded from skeleton sequences. There are several skeleton-based features that
have proven effective in RNN-based and handcrafted-feature-based methods.
However, it remains unknown whether they are suitable for CNN-based approaches.
This paper proposes to encode five spatial skeleton features into images with
different encoding methods. In addition, the performance implication of
different joints used for feature extraction is studied. The proposed method
achieved state-of-the-art performance on NTU RGB+D dataset for 3D human action
analysis. An accuracy of 75.32\% was achieved in Large Scale 3D Human Activity
Analysis Challenge in Depth Videos.
