A review of real-time human action recognition involving vision sensing

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

Human action recognition has been utilized in many applications such as human-computer interaction, video surveillance, assistive living, and gaming. Deployment of human action recognition demands the processing to be carried out in real-time or in a computationally efficient manner. The real-time requirement is addressed by only a subset of the developed methods in the literature. This paper provides a review of computationally efficient human action recognition methods in which a vision sensor is used. The reviewed papers are categorized in terms of conventional and deep learning approaches as well as in terms of single vision and multi-vision modality sensing.

Original languageEnglish
Title of host publicationReal-Time Image Processing and Deep Learning 2021
EditorsNasser Kehtarnavaz, Matthias F. Carlsohn
PublisherSPIE
ISBN (Electronic)9781510643093
DOIs
StatePublished - 2021
EventReal-Time Image Processing and Deep Learning 2021 - Virtual, Online, United States
Duration: Apr 12 2021Apr 16 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11736
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceReal-Time Image Processing and Deep Learning 2021
Country/TerritoryUnited States
CityVirtual, Online
Period04/12/2104/16/21

Keywords

  • Human action recognition
  • Real-time human action recognition
  • Review of vision-based human action recognition

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