Skeleton-based Human Action Recognition via Convolutional Neural Networks (CNN)
This work addresses action recognition for computer vision applications, but it is incremental as it applies an existing method (CNN) to a known problem with specific optimizations.
The paper tackled the problem of skeleton-based human action recognition by proposing a Convolutional Neural Network (CNN) approach, achieving a score of 95% on the NTU-60 dataset, which is comparable to Graph Neural Network (GCN) methods.
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