This paper extends the Spatial-Temporal Graph Convolutional Network (ST-GCN) for
skeleton-based action recognition by introducing two novel modules, namely, the Graph …

Spatio-temporal Graph Convolutional Networks (ST-GCNs) have shown great performance
in the context of skeleton-based action recognition. Nevertheless, ST-GCNs use raw …

In recent years, human action recognition has been studied by many computer vision
researchers. Recent studies have attempted to use two-stream networks using appearance …

Human action recognition (HAR) is an important yet challenging task. This paper presents a
novel method. First, fuzzy weight functions are used in computations of depth motion maps …

Human action recognition remains an important yet challenging task. This work proposes a
novel action recognition system. It uses a novel multi-view region-adaptive multi-resolution …

Center of pressure (CoP) metrics, including CoP path length and sway area, have been
used as gold standard measurements of postural and balance control in biomechanical …

This work presents a new view-invariant action recognition system that is able to classify
human actions by using a single RGB camera, including challenging camera viewpoints …

In this paper, we address the problem of cross-view action recognition from a monocular
RGB camera. This topic has been considered extremely challenging due to the lack of 3D …

Recently, various facilities have been deployed to support visually impaired people.
However, accidents caused by visual disabilities still occur. In this paper, to support the …

This paper considers the emergency behavior detection problem inside an elevator. As
elevators come in different shapes and emergency behavior data are scarce, we propose a …