Although widely used in many applications, accurate and efficient human action recognition
remains a challenging area of research in the field of computer vision. Most recent surveys …

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 …

Human actions can be represented by the trajectories of skeleton joints. Traditional methods
generally model the spatial structure and temporal dynamics of human skeleton with hand …

Human action recognition has a wide range of applications including biometrics,
surveillance, and human computer interaction. The use of multimodal sensors for human …

Graph convolutional models have gained impressive successes on skeleton based human
action recognition task. As graph convolution is a local operation, it cannot fully investigate …

Human activity recognition (HAR) is a very active yet challenging and demanding area of
computer science. Due to the articulated nature of human motion, it is not trivial to detect …

A number of review or survey articles have previously appeared on human action
recognition where either vision sensors or inertial sensors are used individually …

Body joints, directly obtained from a pose estimation model, have proven effective for action
recognition. Existing works focus on analyzing the dynamics of human joints. However …

Skeleton-based action recognition has rapidly become one of the most popular and
essential research topics in computer vision. The task is to analyze the characteristics of …

Automated recognition of human activities or actions has great significance as it
incorporates wide-ranging applications, including surveillance, robotics, and personal …