Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

Video action recognition is widely applied in video indexing, intelligent surveillance,
multimedia understanding, and other fields. Recently, it was greatly improved by …

Video action recognition is one of the representative tasks for video understanding. Over the
last decade, we have witnessed great advancements in video action recognition thanks to …

Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

Video-based human action recognition is currently one of the most active research areas in
computer vision. Various research studies indicate that the performance of action …

Analyzing videos of human actions involves understanding the temporal relationships
among video frames. State-of-the-art action recognition approaches rely on traditional …

The convolutional neural network architecture has different components like convolution and
pooling. The pooling is crucial component placed after the convolution layer. It plays a vital …

We address the problem of domain adaptation in videos for the task of human action
recognition. Inspired by image-based domain adaptation, we can perform video adaptation …

Extending state-of-the-art object detectors from image to video is challenging. The accuracy
of detection suffers from degenerated object appearances in videos, eg, motion blur, video …

Recent years have witnessed the popularity of using recurrent neural network (RNN) for
action recognition in videos. However, videos are of high dimensionality and contain rich …