Human actions being diverse in nature cannot be generalized, thus making it quite difficult to
train a machine to recognize such diversified actions. This challenge is further compounded …

Human action recognition is a challenging task in computer vision. Modeling spatial-
temporal information in videos effectively is crucial for the performance improvement of …

Human activity recognition is an emerging and important area in computer vision which
seeks to determine the activity an individual or group of individuals are performing. The …

Ultra-high-resolution aerial videos are becoming increasingly popular for enhancing
surveillance capabilities in sparsely populated areas. However analyzing human activities …

Given the prevalence of surveillance cameras in our daily lives, human action recognition
from videos holds significant practical applications. A persistent challenge in this field is to …

Advancements in deep neural networks have contributed to near-perfect results for many
computer vision problems, such as object recognition, face recognition, and pose estimation …

Human action recognition based on still images is one of the most challenging computer
vision tasks. In the past decade, convolutional neural networks (CNNs) have developed …

Aerial human action recognition is an emerging topic in drone applications. Commercial
drone platforms capable of detecting basic human actions such as hand gestures have been …

In this paper, we propose a novel approach to video action recognition that integrates a
modified and optimized 3D Convolutional Neural Network, a Long Short-Term Memory …

We Introduce a simple yet effective network that embeds a novel Discriminative Feature
Pooling (DFP) mechanism and a novel Video Segment Attention Model (VSAM), for video …