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

Vision-based Human activity recognition is becoming a trendy area of research due to its
wide application such as security and surveillance, human–computer interactions, patients …

Motion representation plays a vital role in human action recognition in videos. In this study,
we introduce a novel compact motion representation for video action recognition, named …

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

This paper focuses on the temporal aspect for recognizing human activities in videos; an
important visual cue that has long been undervalued. We revisit the conventional definition …

This paper is about temporal segmentation of human actions in videos. We introduce a new
model--temporal deformable residual network (TDRN)--aimed at analyzing video intervals at …

Despite outstanding performance in image recognition, convolutional neural networks
(CNNs) do not yet achieve the same impressive results on action recognition in videos. This …

Two-stream convolutional neural networks have been widely applied to action recognition.
However, two-stream networks are usually adopted to capture spatial information and …

Due to its capacity to gather vast, high-level data about human activity from wearable or
stationary sensors, human activity recognition substantially impacts people's day-to-day …

As characterizing videos simultaneously from spatial and temporal cues has been shown
crucial for the video analysis, the combination of convolutional neural networks and …