Intelligent Video Analytics for Human Action Recognition: The State of Knowledge

The paper presents a comprehensive overview of intelligent video analytics and human action recognition methods. The article provides an overview of the current state of knowledge in the field of human activity recognition, including various techniques such as pose-based, tracking-based, spatio-temp...

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Detalles Bibliográficos
Autores principales: Kulbacki, Marek, Segen, Jakub, Chaczko, Zenon, Rozenblit, Jerzy W., Kulbacki, Michał, Klempous, Ryszard, Wojciechowski, Konrad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181781/
https://www.ncbi.nlm.nih.gov/pubmed/37177461
http://dx.doi.org/10.3390/s23094258
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author Kulbacki, Marek
Segen, Jakub
Chaczko, Zenon
Rozenblit, Jerzy W.
Kulbacki, Michał
Klempous, Ryszard
Wojciechowski, Konrad
author_facet Kulbacki, Marek
Segen, Jakub
Chaczko, Zenon
Rozenblit, Jerzy W.
Kulbacki, Michał
Klempous, Ryszard
Wojciechowski, Konrad
author_sort Kulbacki, Marek
collection PubMed
description The paper presents a comprehensive overview of intelligent video analytics and human action recognition methods. The article provides an overview of the current state of knowledge in the field of human activity recognition, including various techniques such as pose-based, tracking-based, spatio-temporal, and deep learning-based approaches, including visual transformers. We also discuss the challenges and limitations of these techniques and the potential of modern edge AI architectures to enable real-time human action recognition in resource-constrained environments.
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spelling pubmed-101817812023-05-13 Intelligent Video Analytics for Human Action Recognition: The State of Knowledge Kulbacki, Marek Segen, Jakub Chaczko, Zenon Rozenblit, Jerzy W. Kulbacki, Michał Klempous, Ryszard Wojciechowski, Konrad Sensors (Basel) Review The paper presents a comprehensive overview of intelligent video analytics and human action recognition methods. The article provides an overview of the current state of knowledge in the field of human activity recognition, including various techniques such as pose-based, tracking-based, spatio-temporal, and deep learning-based approaches, including visual transformers. We also discuss the challenges and limitations of these techniques and the potential of modern edge AI architectures to enable real-time human action recognition in resource-constrained environments. MDPI 2023-04-25 /pmc/articles/PMC10181781/ /pubmed/37177461 http://dx.doi.org/10.3390/s23094258 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Kulbacki, Marek
Segen, Jakub
Chaczko, Zenon
Rozenblit, Jerzy W.
Kulbacki, Michał
Klempous, Ryszard
Wojciechowski, Konrad
Intelligent Video Analytics for Human Action Recognition: The State of Knowledge
title Intelligent Video Analytics for Human Action Recognition: The State of Knowledge
title_full Intelligent Video Analytics for Human Action Recognition: The State of Knowledge
title_fullStr Intelligent Video Analytics for Human Action Recognition: The State of Knowledge
title_full_unstemmed Intelligent Video Analytics for Human Action Recognition: The State of Knowledge
title_short Intelligent Video Analytics for Human Action Recognition: The State of Knowledge
title_sort intelligent video analytics for human action recognition: the state of knowledge
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181781/
https://www.ncbi.nlm.nih.gov/pubmed/37177461
http://dx.doi.org/10.3390/s23094258
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