A review on machine learning algorithms on human action recognition

Abstract

Human action recognition is a vital field of computer vision research. Its applications incorporate observation frameworks, patient monitoring frameworks, and an assortment of frameworks that include interactions between persons and electronic gadgets, for example, human–computer interfaces. The vast majority of these applications require an automated recognition of abnormal or anomalistic action states, made out of various straightforward (or nuclear) actions of persons. This study gives an overview of different best in class research papers on human movement recognition. Open datasets intended for the assessment of the recognition procedures are also discussed in this paper too, for comparing results of several methodologies on this datasets. We examine both the approaches produced for basic human actions and those for abnormal action states. These methodologies are taxonomically classified based on looking at the points of interest and constraints of every methodology. Space-time volume approaches and sequential methodologies that represent actions and perceive such action sets straightforwardly from images are discussed. Next, hierarchical recognition approaches for abnormal action states are introduced and looked at. Statistic-based methodologies, syntactic methodologies, and description-based methodologies for hierarchical recognition are examined in the paper.