Human action recognition is valuable for numerous practical applications, eg, gaming, video
surveillance, and video search. In this paper we hypothesize that the classification of actions …

Recently, deep convolutional neural networks have demonstrated great effectiveness in
action recognition with both RGB and optical flow in the past decade. However, existing …

Convolutional Neural Networks have achieved excellent successes for object recognition in
still images. However, the improvement of Convolutional Neural Networks over the …

Recognizing actions in videos is not a trivial task because video is an information-intensive
media and includes multiple modalities. Moreover, on each modality, an action may only …

This work addresses the task of action recognition in video sequences. In real world
applications, this task is quite challenging due to the complex background of video content …

Action Recognition is a fundamental task in computer vision field, with a wide range of
applications in autonomous driving, security monitoring, etc. However, previous action …

Research in human action recognition has accelerated significantly since the introduction of
powerful machine learning tools such as Convolutional Neural Networks (CNNs). However …

In the world of action recognition research, one primary focus has been on how to construct
and train networks to model the spatial-temporal volume of an input video. These methods …

Video action recognition is a fundamental task for video understanding. Action recognition in
complex spatio-temporal contexts generally requires fusing of different multi-granularity …

The local key features in video are important for improving the accuracy of human action
recognition. However, most end-to-end methods focus on global feature learning from …