With the widespread application of deep learning in the field of computer vision, deep
learning-based video behavior recognition models have become important tools for video …

Encouraged by the success of convolutional neural networks (CNNs) in image classification,
recently much effort is spent on applying the CNNs to the video-based action recognition …

Image classification and action recognition are both active research topics in the field of
computer vision. However, the development of action recognition is rather slow compared …

A rich video data representation can be realized by means of spatio-temporal frequency
analysis. In this research study we show that a video can be disentangled, following the …

Two-stream convolutional neural networks show great promise for action recognition tasks.
However, most two-stream based approaches train the appearance and motion …

In recent years, Internet of Things (IoT) has made rapid development, and IoT devices are
developing toward intelligence. IoT terminal devices represented by surveillance cameras …

Compressed video action recognition classifies actions using multiple features stored in
compressed videos to omit the decoding process for RGB frames and shorten the …

Effective spatiotemporal feature representation is crucial to the video-based action
recognition task. Focusing on discriminate spatiotemporal feature learning, we propose …

Videos naturally contain dynamic variation over the temporal axis, which will result in the
same visual clues (eg, semantics, objects) changing their scale, position, and perspective …

Training a model to recognize human actions in videos is computationally intensive. While
modern strategies employ transfer learning methods to make the process more efficient, they …