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A biologically inspired spiking neural P system in selective visual attention for efficient feature extraction from human motion

Nowadays, human action recognition has become an essential task in health care and other fields. During the last decade, several authors have developed algorithms for human activity detection and recognition by exploiting at the maximum the high-performance computing devices to improve the quality a...

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Autores principales: Anides, Esteban, Garcia, Luis, Sanchez, Giovanny, Avalos, Juan-Gerardo, Abarca, Marco, Frias, Thania, Vazquez, Eduardo, Juarez, Emmanuel, Trejo, Carlos, Hernandez, Derlis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9538564/
https://www.ncbi.nlm.nih.gov/pubmed/36212613
http://dx.doi.org/10.3389/frobt.2022.1028271
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author Anides, Esteban
Garcia, Luis
Sanchez, Giovanny
Avalos, Juan-Gerardo
Abarca, Marco
Frias, Thania
Vazquez, Eduardo
Juarez, Emmanuel
Trejo, Carlos
Hernandez, Derlis
author_facet Anides, Esteban
Garcia, Luis
Sanchez, Giovanny
Avalos, Juan-Gerardo
Abarca, Marco
Frias, Thania
Vazquez, Eduardo
Juarez, Emmanuel
Trejo, Carlos
Hernandez, Derlis
author_sort Anides, Esteban
collection PubMed
description Nowadays, human action recognition has become an essential task in health care and other fields. During the last decade, several authors have developed algorithms for human activity detection and recognition by exploiting at the maximum the high-performance computing devices to improve the quality and efficiency of their results. However, in real-time and practical human action recognition applications, the simulation of these algorithms exceed the capacity of current computer systems by considering several factors, such as camera movement, complex scene and occlusion. One potential solution to decrease the computational complexity in the human action detection and recognition can be found in the nature of the human visual perception. Specifically, this process is called selective visual attention. Inspired by this neural phenomena, we propose for the first time a spiking neural P system for efficient feature extraction from human motion. Specifically, we propose this neural structure to carry out a pre-processing stage since many studies have revealed that an analysis of visual information of the human brain proceeds in a sequence of operations, in which each one is applied to a specific location or locations. In this way, this specialized processing have allowed to focus the recognition of the objects in a simpler manner. To create a compact and high speed spiking neural P system, we use their cutting-edge variants, such as rules on the synapses, communication on request and astrocyte-like control. Our results have demonstrated that the use of the proposed neural P system increases significantly the performance of low-computational complexity neural classifiers up to more 97% in the human action recognition.
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spelling pubmed-95385642022-10-08 A biologically inspired spiking neural P system in selective visual attention for efficient feature extraction from human motion Anides, Esteban Garcia, Luis Sanchez, Giovanny Avalos, Juan-Gerardo Abarca, Marco Frias, Thania Vazquez, Eduardo Juarez, Emmanuel Trejo, Carlos Hernandez, Derlis Front Robot AI Robotics and AI Nowadays, human action recognition has become an essential task in health care and other fields. During the last decade, several authors have developed algorithms for human activity detection and recognition by exploiting at the maximum the high-performance computing devices to improve the quality and efficiency of their results. However, in real-time and practical human action recognition applications, the simulation of these algorithms exceed the capacity of current computer systems by considering several factors, such as camera movement, complex scene and occlusion. One potential solution to decrease the computational complexity in the human action detection and recognition can be found in the nature of the human visual perception. Specifically, this process is called selective visual attention. Inspired by this neural phenomena, we propose for the first time a spiking neural P system for efficient feature extraction from human motion. Specifically, we propose this neural structure to carry out a pre-processing stage since many studies have revealed that an analysis of visual information of the human brain proceeds in a sequence of operations, in which each one is applied to a specific location or locations. In this way, this specialized processing have allowed to focus the recognition of the objects in a simpler manner. To create a compact and high speed spiking neural P system, we use their cutting-edge variants, such as rules on the synapses, communication on request and astrocyte-like control. Our results have demonstrated that the use of the proposed neural P system increases significantly the performance of low-computational complexity neural classifiers up to more 97% in the human action recognition. Frontiers Media S.A. 2022-09-23 /pmc/articles/PMC9538564/ /pubmed/36212613 http://dx.doi.org/10.3389/frobt.2022.1028271 Text en Copyright © 2022 Anides, Garcia, Sanchez, Avalos, Abarca, Frias, Vazquez, Juarez, Trejo and Hernandez. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Anides, Esteban
Garcia, Luis
Sanchez, Giovanny
Avalos, Juan-Gerardo
Abarca, Marco
Frias, Thania
Vazquez, Eduardo
Juarez, Emmanuel
Trejo, Carlos
Hernandez, Derlis
A biologically inspired spiking neural P system in selective visual attention for efficient feature extraction from human motion
title A biologically inspired spiking neural P system in selective visual attention for efficient feature extraction from human motion
title_full A biologically inspired spiking neural P system in selective visual attention for efficient feature extraction from human motion
title_fullStr A biologically inspired spiking neural P system in selective visual attention for efficient feature extraction from human motion
title_full_unstemmed A biologically inspired spiking neural P system in selective visual attention for efficient feature extraction from human motion
title_short A biologically inspired spiking neural P system in selective visual attention for efficient feature extraction from human motion
title_sort biologically inspired spiking neural p system in selective visual attention for efficient feature extraction from human motion
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9538564/
https://www.ncbi.nlm.nih.gov/pubmed/36212613
http://dx.doi.org/10.3389/frobt.2022.1028271
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