Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

Abstract Human Action Recognition (HAR) involves human activity monitoring task in
different areas of medical, education, entertainment, visual surveillance, video retrieval, as …

Human skeleton-based action recognition offers a valuable means to understand the
intricacies of human behavior because it can handle the complex relationships between …

Estimating 3D human poses from monocular videos is a challenging task due to depth
ambiguity and self-occlusion. Most existing works attempt to solve both issues by exploiting …

Action recognition with skeleton data is attracting more attention in computer vision.
Recently, graph convolutional networks (GCNs), which model the human body skeletons as …

In the semi-supervised skeleton-based action recognition task, obtaining more
discriminative information from both labeled and unlabeled data is a challenging problem …

In skeleton-based action recognition, graph convolutional networks (GCNs) have achieved
remarkable success. Nevertheless, how to efficiently model the spatial-temporal skeleton …

Research on depth-based human activity analysis achieved outstanding performance and
demonstrated the effectiveness of 3D representation for action recognition. The existing …

Action recognition with skeleton data has recently attracted much attention in computer
vision. Previous studies are mostly based on fixed skeleton graphs, only capturing local …

Abstract Skeleton-based Human Activity Recognition has achieved great interest in recent
years as skeleton data has demonstrated being robust to illumination changes, body scales …