For a long time, learning spatiotemporal features with deep neural networks has been a
difficult task in the field of computer vision. In this paper, we present a novel deep …

Action recognition is one of the top challenges in computer vision. In this paper, we present
two binary-based video descriptors with outstanding characteristics in terms of recognition …

In this paper, we propose the use of dynamic-images-based approach for action recognition.
Specifically, we exploit the multimodal information recorded by a Kinect sensor (RGB-D and …

Video action recognition is one of the topics for video understanding. Over the past decade,
video action recognition has made great progress due to the emergence of deep learning …

The active human action motion characteristics from moving areas within a particular
spectrum are conveyed by the information extraction domain of video data. Meanwhile, most …

We propose a novel approach for online action recognition. The action is represented in a
low dimensional (15D) space using a covariance descriptor of shape and motion features …

Video action recognition is a vital area of computer vision. By adding temporal dimension
into convolution structure, 3D convolution neural network owns the capacity to extract spatio …

The launching of low-cost depth sensors opens up new potentials for RGB-D-based human
action recognition. However, most of current RGB-D-based methods simply fuse multimodal …

Video action recognition refers to the process of recognizing human actions in a video. The
field of action recognition encompasses a wide variety of high-impact societal applications …

Video-based action recognition with deep neural networks has shown remarkable progress.
However, most of the existing approaches are too computationally expensive due to the …