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 …

With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …

Recurrent neural networks (RNNs) have been widely used for processing sequential data.
However, RNNs are commonly difficult to train due to the well-known gradient vanishing and …

Skeleton-based human action recognition has recently attracted increasing attention thanks
to the accessibility and the popularity of 3D skeleton data. One of the key challenges in …

Human action recognition based on skeletons has wide applications in human–computer
interaction and intelligent surveillance. However, view variations and noisy data bring …

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 …

Human action recognition in 3D skeleton sequences has attracted a lot of research attention.
Recently, long short-term memory (LSTM) networks have shown promising performance in …

In this work, we address human activity and hand gesture recognition problems using 3D
data sequences obtained from full-body and hand skeletons, respectively. To this aim, we …

Security inspection often deals with a piece of baggage or suitcase where objects are
heavily overlapped with each other, resulting in an unsatisfactory performance for prohibited …

Most video-based action recognition approaches choose to extract features from the whole
video to recognize actions. The cluttered background and non-action motions limit the …