For skeleton-based action recognition, most of the existing works used recurrent neural
networks. Using convolutional neural networks (CNNs) is another attractive solution …

Human Action Recognition (HAR) is a branch of computer vision that deals with the
identification of human actions at various levels including low level, action level, and …

Skeleton-based action recognition has recently achieved much attention since they can
robustly convey the action information. Recently, many studies have shown that graph …

Skeleton-based human action recognition has been a popular research field during the past
few years. With the help of cameras equipping deep sensors, such as the Kinect, human …

Much of the existing work on action recognition combines simple features with complex
classifiers or models to represent an action. Parameters of such models usually do not have …

Active research has been carried out for human action recognition using 3D human skeleton
joints with the release of cost-efficient RGB-D sensors. However, extracting discriminative …

Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted
increasing attention since the first work reported in 2010. Over this period, many benchmark …

The launching of low-cost depth sensors opens up new potentials for RGB-D-based human
action recognition. However, most of current RGB-D-based methods simply fuse multimodal …

In this paper, we study current and upcoming frontiers across the landscape of skeleton-
based human action recognition. To study skeleton-action recognition in the wild, we …

Different modalities extracted from videos, such as RGB and optical flows, may provide
complementary cues for improving video action recognition. In this paper, we introduce a …