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

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 activity recognition (HAR) systems attempt to automatically identify and analyze
human activities using acquired information from various types of sensors. Although several …

Dynamics of human body skeletons convey significant information for human action
recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted …

Abstract Long Short-Term Memory (LSTM) networks have shown superior performance in
3D human action recognition due to their power in modeling the dynamics and …

What do infants know? How does the knowledge that they begin with prepare them for
learning about the particular physical, cultural, and social world in which they live? Answers …

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 …

Human actions can be represented by the trajectories of skeleton joints. Traditional methods
generally model the spatial structure and temporal dynamics of human skeleton with hand …

Recently introduced cost-effective depth sensors coupled with the real-time skeleton
estimation algorithm of Shotton et al.[16] have generated a renewed interest in skeleton …

In recent years, there has been a proliferation of works on human action classification from
depth sequences. These works generally present methods and/or feature representations …