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 …

Recently, develo** automated video surveillance systems (VSSs) has become crucial to
ensure the security and safety of the population, especially during events involving large …

Automated human posture estimation (A-HPE) systems need delicate methods for detecting
body parts and selecting cues based on marker-less sensors to effectively recognize …

Research in human activity recognition (HAR) has seen tremendous growth and
continuously receiving attention from both the Computer Vision and the Image Processing …

Existing deep learning methods for action recognition in videos require a large number of
labeled videos for training, which is labor-intensive and time-consuming. For the same …

This letter proposes an ensemble neural network (Ensem-NN) for skeleton-based action
recognition. The Ensem-NN is introduced based on the idea of ensemble learning,“two …

Action Recognition is a fundamental task in computer vision field, with a wide range of
applications in autonomous driving, security monitoring, etc. However, previous action …

Unsupervised domain adaptation (UDA) aims to recognize newly emerged patterns in target
domains, which may be unlabeled, by leveraging knowledge from patterns learnt from …

Human action recognition is crucial to many practical applications, ranging from human-
computer interaction to video surveillance. Most approaches either recognize the human …

Graph Convolutional Networks have been successfully applied in skeleton-based action
recognition. The key is fully exploring the spatial-temporal context. This letter proposes a …