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.

本研究的目的是通过使用深度图像采集的骨架数据，创建一个剧场人类行为识别系统，以帮助视障人士和盲人。利用三种预训练的基于骨架的人类行为识别 Spatio-Temporal Graph Convolution Networks 在源领域和目标领域之间建立迁移学习模型，结果表明，迁移学习有助于提高剧场环境下的人类动作识别系统的性能。