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 …

Human action recognition systems use data collected from a wide range of sensors to
accurately identify and interpret human actions. One of the most challenging issues for …

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace
due to the convenience, safety, and economic benefits. Although a number of surveys have …

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

Spatial-temporal graphs have been widely used by skeleton-based action recognition
algorithms to model human action dynamics. To capture robust movement patterns from …

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 …

The skeleton data have been widely used for the action recognition tasks since they can
robustly accommodate dynamic circumstances and complex backgrounds. In existing …

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 …