Abstract Human Action Recognition (HAR) involves human activity monitoring task in
different areas of medical, education, entertainment, visual surveillance, video retrieval, as …

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 action recognition plays a fundamental role in the design of smart solution for home
environments, particularly in relation to ambient assisted living applications, where the …

We present a method for predicting dense depth in scenarios where both a monocular
camera and people in the scene are freely moving. Existing methods for recovering depth for …

Gesture is a natural interface in human-computer interaction, especially interacting with
wearable devices, such as VR/AR helmet and glasses. However, in the gesture recognition …

In this paper, we focus on heterogeneous feature learning for RGB-D activity recognition.
Considering that features from different channels could share some similar hidden …

In this paper, we develop a vision-based system that employs a combined RGB and depth
descriptor to classify hand gestures. The method is studied for a human-machine interface …

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 …

The aim of Active and Assisted Living is to develop tools to promote the ageing in place of
elderly people, and human activity recognition algorithms can help to monitor aged people …

Inspired by the success of deep learning methods, for human activity recognition based on
individual vision cues, this paper presents a ConvNets based approach for activity …