Abstract Convolutional Neural Networks (CNNs) usually use top-level appearance features
of video frames for action recognition. However, these methods discard the implicit …

Spatiotemporal and motion features are two complementary and crucial information for
video action recognition. Recent state-of-the-art methods adopt a 3D CNN stream to learn …

Human action recognition in video is one of the most widely applied topics in the field of
image and video processing, with many applications in surveillance (security, sports, etc.) …

Recent years have witnessed the popularity of using two-stream convolutional neural
networks for action recognition. However, existing two-stream convolutional neural network …

In this paper, we propose a novel high-level action representation using joint spatial-
temporal attention model, with application to video-based human action recognition …

Activity recognition which aims to accurately distinguish human actions in complex
environments plays a key role in human-robot/computer interaction. However, long-lasting …

In order to obtain global contextual information precisely from videos with heavy camera
motions and scene changes, this study proposes an improved spatiotemporal two-stream …

This paper addresses the issue of video-based action recognition by exploiting an advanced
multistream convolutional neural network (CNN) to fully use semantics-derived multiple …

Two-stream convolutional networks plays an essential role as a powerful feature extractor in
human action recognition in videos. Recent studies have shown the importance of two …

Recently, deep convolutional networks (ConvNets) have achieved remarkable progress for
action recognition in videos. Most existing deep frameworks treat a video as an unordered …