This article aims to tackle the problem of group activity recognition in the multiple-person
scene. To model the group activity with multiple persons, most long short-term memory …

Human motion prediction aims to generate future motions based on the observed human
motions. Witnessing the success of Recurrent Neural Networks (RNN) in modeling …

RNN-based approaches have achieved outstanding performance on action recognition with
skeleton inputs. Currently these methods limit their inputs to coordinates of joints and …

This work aims to address the group activity recognition problem by exploring human motion
characteristics. Traditional methods hold that the motions of all persons contribute equally to …

Massive open online courses and other forms of remote education continue to increase in
popularity and reach. The ability to efficiently proctor remote online examinations is an …

We consider inferring the future actions of people from a still image or a short video clip.
Predicting future actions before they are actually executed is a critical ingredient for enabling …

In this work, we aim to address the problem of human interaction recognition in videos by
exploring the long-term inter-related dynamics among multiple persons. Recently, Long …

Recent skeleton-based action recognition approaches achieve great improvement by using
recurrent neural network (RNN) models. Currently, these approaches build an end-to-end …

In this paper, we develop a new model for recognizing human actions. An action is modeled
as a very sparse sequence of temporally local discriminative keyframes collections of partial …

Human Action Recognition (HAR) for CCTV-oriented applications is still a challenging
problem. Real-world scenarios HAR implementations is difficult because of the gap between …