A REVIEW ON MACHINE LEARNING ALGORITHMS ON HUMAN ACTION RECOGNITION


Ankush Rai, Jagadeesh Kannan R

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. Statistics based methodologies, syntactic methodologies, and description based methodologies for hierarchical recognition is examined in the paper.


Keywords


Algorithms, computer vision; human activity recognition; event detection; activity analysis; video recognition.

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About this article

Title

A REVIEW ON MACHINE LEARNING ALGORITHMS ON HUMAN ACTION RECOGNITION

Keywords

Algorithms, computer vision; human activity recognition; event detection; activity analysis; video recognition.

DOI

10.22159/ajpcr.2017.v10s1.19977

Date

01-04-2017

Additional Links

Manuscript Submission

Journal

Asian Journal of Pharmaceutical and Clinical Research
Special Issue April 2017 Page: 406-416

Print ISSN

0974-2441

Online ISSN

2455-3891

Statistics

159 Views | 94 Downloads

Authors & Affiliations

Ankush Rai
School of Computer Science & Engineering, VIT University, Chennai, Tamil Nadu, India.
India

Jagadeesh Kannan R
School of Computer Science & Engineering, VIT University, Chennai, Tamil Nadu, India.
India


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