  The aim of this research is to recognize human actions performed on stage to
aid visually impaired and blind individuals. To achieve this, we have created a
theatre human action recognition system that uses skeleton data captured by
depth image as input. We collected new samples of human actions in a theatre
environment, and then tested the transfer learning technique with three
pre-trained Spatio-Temporal Graph Convolution Networks for skeleton-based human
action recognition: the spatio-temporal graph convolution network, the
two-stream adaptive graph convolution network, and the multi-scale disentangled
unified graph convolution network. We selected the NTU-RGBD human action
benchmark as the source domain and used our collected dataset as the target
domain. We analyzed the transferability of the pre-trained models and proposed
two configurations to apply and adapt the transfer learning technique to the
diversity between the source and target domains. The use of transfer learning
helped to improve the performance of the human action system within the context
of theatre. The results indicate that Spatio-Temporal Graph Convolution
Networks is positively transferred, and there was an improvement in performance
compared to the baseline without transfer learning.
