A5093815259- Human Pose and Action Recognition
- Anomaly Detection Techniques and Applications
- Artificial Intelligence in Healthcare
- Context-Aware Activity Recognition Systems
- Gait Recognition and Analysis
King Abdulaziz University
2024
Skeleton-based human action recognition is a challenging yet important technique because of its wide range applications in many fields, including patient monitoring, security surveillance, and observing human-machine interactions. Many algorithms that attempt to distinguish between types activities have been proposed. However, most practical require highly accurate detection specific activities. In this study, novel spatiotemporal graph autoencoder network for skeleton-based Furthermore, an...
Human action recognition (HAR) based on skeleton data is a challenging yet important technique because of its wide range applications in many fields, including patient monitoring, security surveillance, and observing human-machine interactions. Many algorithms that attempt to distinguish between types activities have been proposed. However, most practical require highly accurate detection specific activities. In this study, novel spatiotemporal graph autoencoder network for HAR Furthermore,...
The task of human action recognition (HAR) based on skeleton data is a challenging yet crucial technique owing to its wide-ranging applications in numerous domains, including patient monitoring, security surveillance, and observation human-machine interactions. While algorithms have been proposed an attempt distinguish between myriad activities, most practical necessitate highly accurate detection specific activity types. This study proposes novel spatiotemporal graph autoencoder network for...
Human action recognition (HAR) based on skeleton data is a challenging yet crucial task due to its wide-ranging applications, including patient monitoring, security surveillance, and human- machine interaction. Although numerous algorithms have been proposed distinguish between various activities, most practical applications require highly accurate detection of specific actions. In this study, we propose novel, spatiotemporal graph autoencoder network for HAR, designated as GA-GCN....