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 …

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 activity recognition (HAR) systems attempt to automatically identify and analyze
human activities using acquired information from various types of sensors. Although several …

Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

Most natural videos contain numerous events. For example, in a video of a" man playing a
piano", the video might also contain" another man dancing" or" a crowd clap**". We …

Detecting actions in untrimmed videos is an important yet challenging task. In this paper, we
present the structured segment network (SSN), a novel framework which models the …

Deep convolutional networks have achieved great success for visual recognition in still
images. However, for action recognition in videos, the advantage over traditional methods is …

We present a general and flexible video-level framework for learning action models in
videos. This method, called temporal segment network (TSN), aims to model long-range …

Current state-of-the-art approaches for few-shot action recognition achieve promising
performance by conducting frame-level matching on learned visual features. However, they …

This paper presents a new method for 3D action recognition with skeleton sequences (ie, 3D
trajectories of human skeleton joints). The proposed method first transforms each skeleton …