Abstract Human activity recognition has been an open problem in computer vision for almost 2 decades. During this time, there have many approaches proposed to solve problem, but very few managed it a way that is sufficiently computationally efficient real-time applications. Recently, changed, with keypoint-based methods demonstrating high degree of accuracy low computational cost. These take gi...

Abstract Human Activity Recognition has been a favorite topic for the scholars not only because of its wide scale acceptance in industry but areas which may help medical and our normal household works as well. Since to make this technology available last person standing queue it is important that models compiled trained field are just high performing optimized such with incurs least overhead. A...

In this letter we address the task of recognizing assembly actions as a structure (e.g. piece furniture or toy block tower) is built up from set primitive objects. Recognizing full range requires perception at level spatial detail that has not been attempted in action recognition literature to date. We extend fine-grained activity setting its generality by unifying and kinematic structures with...

The paper considers the problem solution of classifying type physical activity a person according to visual data. authors propose using deep neural networks determine activity. recognizing human from video data or single image systems are currently actively used in various areas As example we can take system for monitoring effectiveness enterprise employees. So solving actions is an actual task...

Numerous methods for human activity recognition (HAR) have been proposed in the past two decades. Many of these are based on sparse representations, which describe whole video content by a set local features. Trajectories, as mid-level features, capable describing movements interest points two-dimensional (2D) space. However, 2D trajectories might be affected viewpoint changes, potentially decr...