Spatiotemporal human representation based on 3D visual perception data is a rapidly
growing research area. Representations can be broadly categorized into two groups …

Multi-instance learning (MIL) is more general and challenging than traditional supervised
learning in that labels are given at the bag level. The popular feature mapping approaches …

Recently introduced cost-effective depth sensors coupled with real-time skeleton extraction
algorithms have generated a renewed interest in skeleton-based human action recognition …

Based on the depth information and skeleton tracking technology of Microsoft Kinectv2
sensor, this paper performs human body gesture recognition and realizes human fall …

Human activity recognition in real time is a challenging task. Recently, a plethora of studies
has been proposed using deep learning architectures. The implementation of these …

Activity prediction is an essential task in practical human-centered robotics applications,
such as security, assisted living, etc., which is targeted at inferring ongoing human activities …

This paper aims to determine which is the best human action recognition method based on
features extracted from RGB-D devices, such as the Microsoft Kinect. A review of all the …

There is a large variation in the activities that humans perform in their everyday lives. We
consider modeling these composite human activities which comprises multiple basic level …

Previous work on 3D action recognition has focused on using hand-designed features,
either from depth videos or 2D videos. In this work, we present an effective way to combine …

Activity prediction is an essential task in practical human-centered robotics applications,
such as security, assisted living, etc., which targets at inferring ongoing human activities …