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

Digitization of human motion using skeleton representations offers exciting possibilities for a
large number of applications but, at the same time, requires innovative techniques for their …

In this paper, we present a method (Action-Fusion) for human action recognition from depth
maps and posture data using convolutional neural networks (CNNs). Two input descriptors …

In contrast to the widely studied problem of recognizing an action given a complete
sequence, action anticipation aims to identify the action from only partially available videos …

The recently developed event cameras can directly sense the motion in the scene by
generating an asynchronous sequence of events, ie, event streams, where each individual …

Over the last few years, with the immense popularity of the Kinect, there has been renewed
interest in developing methods for human gesture and action recognition from 3D skeletal …

From just a short glance at a video, we can often tell whether a person's action is intentional
or not. Can we train a model to recognize this? We introduce a dataset of in-the-wild videos …

In this work, we present the RNN Tree (RNN-T), an adaptive learning framework for skeleton
based human action recognition. Our method categorizes action classes and uses multiple …

Movement primitive segmentation enables long sequences of human movement
observation data to be segmented into smaller components, termed movement primitives, to …

This paper focuses on narratives published by UK companies, defined here as the content of
annual reports excluding financial statements and notes to accounts. We endeavour to …