Human action recognition is one of the hottest topics in the research field, so there are many
relevant review papers illustrating the multi-modality of data, the selection of feature vectors …

Human activity recognition (HAR) is an important research problem in computer vision. This
problem is widely applied to building applications in human–machine interactions …

In the task of skeleton-based action recognition, long-term temporal dependencies are
significant cues for sequential skeleton data. State-of-the-art methods rarely have access to …

Skeleton-based human action recognition has attracted increasing attention and many
methods have been proposed to boost the performance. However, these methods still …

Human action recognition has been applied in many fields, such as video surveillance and
human computer interaction, where it helps to improve performance. Numerous reviews of …

Graph convolutional networks (GCNs) can well-preserve the structure information of the
human body. They have achieved outstanding performance in skeleton-based action …

Action recognition based on 3D skeleton data has attracted much attention due to its wide
application, and it is one of the most popular research topics in computer vision. The 3D …

Graph convolutional networks (GCN) have attracted increasing interest in action recognition
in recent years. GCN models human skeleton sequences as spatio-temporal graphs. Also …

Human pose estimation refers to accurately estimating the position of the human body from
a single RGB image and detecting the location of the body. It serves as the basis for several …

Skeleton-based action recognition methods using complete human skeletons have
achieved remarkable performance, but the performance of these methods could significantly …