Abstract Although synthetic training data has been shown to be beneficial for tasks such as human pose estimation, its use RGB action recognition is relatively unexplored. Our goal in this work answer the question whether humans can improve performance of , with a particular focus on generalization unseen viewpoints. We make recent advances monocular 3D body reconstruction from real sequences a...

Human action recognition is one of the most important topics in computer vision. Monitoring elderly people and children, smart surveillance systems human-computer interaction are a few examples its applications. The aim this study to recognize human activities by utilizing phase information extracted from frequency domain video data as handcrafted features. Rather than estimating optical flow o...

Efficiently modeling spatial–temporal information in videos is crucial for action recognition. To achieve this goal, state-of-the-art methods typically employ the convolution operator and dense interaction modules such as non-local blocks. However, these cannot accurately fit diverse events videos. On one hand, adopted convolutions are with fixed scales, thus struggling of various scales. other...

Human detection and action recognition form the basis for understanding human behaviors. Human detection is used to detect the positions of humans, and action recognition is able to recognize the action of specific humans. However, numerous approaches have been used to handle action recognition and human detection separately. Therefore, three main issues still exist when independent methods of ...