Abstract Human Activity Recognition (HAR) plays a significant role in the everyday life of
people because of its ability to learn extensive high-level information about human activity …

Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted
increasing attention since the first work reported in 2010. Over this period, many benchmark …

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 …

Human activity recognition (HAR) is a complex and multifaceted problem. The research
community has reported numerous approaches to perform HAR. Along with HAR …

Three-dimensional hand gesture recognition has attracted increasing research interests in
computer vision, pattern recognition, and human-computer interaction. The emerging depth …

Human action recognition in video analytics has been widely studied in recent years. Yet,
most of these methods assign a single action label to video after either analyzing a complete …

The interest in action and gesture recognition has grown considerably in the last years. In
this paper, we present a survey on current deep learning methodologies for action and …

In this work, we propose an approach to the spatiotemporal localisation (detection) and
classification of multiple concurrent actions within temporally untrimmed videos. Our …

Despite significant progress in the development of human action detection datasets and
algorithms, no current dataset is representative of real-world aerial view scenarios. We …

Benchmark datasets that measure camera pose accuracy have driven progress in visual re-
localisation research. To obtain poses for thousands of images, it is common to use a …