Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Although widely used in many applications, accurate and efficient human action recognition
remains a challenging area of research in the field of computer vision. Most recent surveys …

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

Research on depth-based human activity analysis achieved outstanding performance and
demonstrated the effectiveness of 3D representation for action recognition. The existing …

Recent approaches in depth-based human activity analysis achieved outstanding
performance and proved the effectiveness of 3D representation for classification of action …

In this paper, we propose a deep progressive reinforcement learning (DPRL) method for
action recognition in skeleton-based videos, which aims to distil the most informative frames …

Skeleton-based human action recognition has recently attracted increasing attention thanks
to the accessibility and the popularity of 3D skeleton data. One of the key challenges in …

In the last few years, deep learning has led to very good performance on a variety of
problems, such as visual recognition, speech recognition and natural language processing …

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 …

Skeleton-based human action recognition has recently attracted increasing attention due to
the popularity of 3D skeleton data. One main challenge lies in the large view variations in …