Version 1
: Received: 28 January 2024 / Approved: 29 January 2024 / Online: 29 January 2024 (08:19:58 CET)
Version 2
: Received: 18 February 2024 / Approved: 20 February 2024 / Online: 21 February 2024 (03:46:16 CET)
How to cite:
Abduljalil, H.; Elhayek, A.; Marish Ali, A.; Alsolami, F. Spatiotemporal Graph Autoencoder Network for Skeleton-Based Human Action Recognition. Preprints2024, 2024011998. https://doi.org/10.20944/preprints202401.1998.v2
Abduljalil, H.; Elhayek, A.; Marish Ali, A.; Alsolami, F. Spatiotemporal Graph Autoencoder Network for Skeleton-Based Human Action Recognition. Preprints 2024, 2024011998. https://doi.org/10.20944/preprints202401.1998.v2
Abduljalil, H.; Elhayek, A.; Marish Ali, A.; Alsolami, F. Spatiotemporal Graph Autoencoder Network for Skeleton-Based Human Action Recognition. Preprints2024, 2024011998. https://doi.org/10.20944/preprints202401.1998.v2
APA Style
Abduljalil, H., Elhayek, A., Marish Ali, A., & Alsolami, F. (2024). Spatiotemporal Graph Autoencoder Network for Skeleton-Based Human Action Recognition. Preprints. https://doi.org/10.20944/preprints202401.1998.v2
Chicago/Turabian Style
Abduljalil, H., Abdullah Marish Ali and Fawaz Alsolami. 2024 "Spatiotemporal Graph Autoencoder Network for Skeleton-Based Human Action Recognition" Preprints. https://doi.org/10.20944/preprints202401.1998.v2
Abstract
Human action recognition (HAR) based on skeleton data is a challenging yet important technique because of its wide range of applications in many fields, including patient monitoring, security surveillance, and observing human-machine interactions. Many algorithms that attempt to distinguish between many types of activities have been proposed. However, most practical applications require highly accurate detection of specific types of activities. In this study, a novel and highly accurate spatiotemporal graph autoencoder network for HAR based on skeleton data is proposed. Furthermore, an extensive study was conducted using different modalities. For this purpose, a spatiotemporal graph autoencoder that automatically learns spatial as well as temporal patterns from human skeleton datasets was built. The powerful graph convolutional network named GA-GCN, developed in this study, notably outperforms most of the existing state-of-the-art methods based on two common datasets, namely NTU RGB+D and NTU RGB+D 120 . On the first dataset, we achieved an accuracy of 92.3% and 96.7% based on the cross-subject and cross-view evaluations, respectively. On the other more challenging dataset (i.e. NTU RGB+D 120), GA-GCN achieved 88.8% and 90.4% based on the cross-subject and cross-set evaluation, respectively.
Keywords
graph convolutional networks; graph autoencoder; deep learning; human activity analysis; skeleton-based human action recognition
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
