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 …

Human action recognition in video analytics has been widely studied in recent years. Yet,
most of these methods assign a single action label to video after either analyzing a complete …

The interest in action and gesture recognition has grown considerably in the last years. In
this paper, we present a survey on current deep learning methodologies for action and …

Understanding human activity is very challenging even with the recently developed
3D/depth sensors. To solve this problem, this work investigates a novel deep structured …

Human activity understanding with 3D/depth sensors has received increasing attention in
multimedia processing and interactions. This work targets on developing a novel deep …

Evaluating the performance of computer vision algorithms is classically done by reporting
classification error or accuracy, if the problem at hand is the classification of an object in an …

Interest in automatic action and gesture recognition has grown considerably in the last few
years. This is due in part to the large number of application domains for this type of …

In this paper, a novel action recognition method is proposed based on hierarchical dynamic
Bayesian network (HDBN). The algorithm is divided into system learning stage and action …

Patients' behaviors in the Intensive Care Units (ICU) have garnered research attention,
particularly regarding the impact of Unplanned Extubation (UEX). However, there is currently …

A deep discriminative structured model, convolutional neural random fields (CNRFs), is
proposed for action recognition problem. In the proposed model, a spatio-temporal …