Understanding human actions in visual data is tied to advances in complementary research
areas including object recognition, human dynamics, domain adaptation and semantic …

Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

Wearable sensors in the smart home environment have been actively developed as
assistive systems to detect behavioral anomalies. Smart wearable devices incorprated into …

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 …

As computers are becoming more and more a part of our everyday life, the vision of Mark
Weiser about ubiquitous computing becomes true. One of the core tasks of such devices is …

Action recognition from videos is an important area of computer vision research due to its
various applications, ranging from visual surveillance to human-computer interaction. To …

We present a review on the current state of publicly available datasets within the human
action recognition community; highlighting the revival of pose based methods and recent …

Providing ground truth is essential for activity recognition and behaviour analysis as it is
needed for providing training data in methods of supervised learning, for providing context …

Providing ground truth is essential for activity recognition for three reasons: to apply methods
of supervised learning, to provide context information for knowledge-based methods, and to …

Motivated by the powerful capability of deep neural networks in feature learning, a new
graph-based neural network is proposed to learn local and global relational information on …