Recently, there has been a remarkable increase in the interest towards
skeleton-based action recognition within the research community, owing to its
various advantageous features, including computational efficiency,
representative features, and illumination invariance. Despite this, researchers
continue to explore and investigate the most optimal way to represent human
actions through skeleton representation and the extracted features. As a
result, the growth and availability of human action recognition datasets have
risen substantially. In addition, deep learning-based algorithms have gained
widespread popularity due to the remarkable advancements in various computer
vision tasks. Most state-of-the-art contributions in skeleton-based action
recognition incorporate a Graph Neural Network (GCN) architecture for
representing the human body and extracting features. Our research demonstrates
that Convolutional Neural Networks (CNNs) can attain comparable results to GCN,
provided that the proper training techniques, augmentations, and optimizers are
applied. Our approach has been rigorously validated, and we have achieved a
score of 95% on the NTU-60 dataset

本文探讨了基于骨架的动作识别在人类动作识别数据集方面的优势，以及深度学习算法在该领域中的应用。同时，研究表明，通过适当的训练技巧、数据增强和优化器，卷积神经网络可以达到与图神经网络相媲美的效果。实验在 NTU-60 数据集上取得了 95% 的准确度。