Cargando…

Bioinspired Garra Rufa Optimization-Assisted Deep Learning Model for Object Classification on Pedestrian Walkways

Object detection in pedestrian walkways is a crucial area of research that is widely used to improve the safety of pedestrians. It is not only challenging but also a tedious process to manually examine the labeling of abnormal actions, owing to its broad applications in video surveillance systems an...

Descripción completa

Detalles Bibliográficos
Autores principales: Yang, Eunmok, Shankar, K., Kumar, Sachin, Seo, Changho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669902/
https://www.ncbi.nlm.nih.gov/pubmed/37999182
http://dx.doi.org/10.3390/biomimetics8070541
_version_ 1785139800861310976
author Yang, Eunmok
Shankar, K.
Kumar, Sachin
Seo, Changho
author_facet Yang, Eunmok
Shankar, K.
Kumar, Sachin
Seo, Changho
author_sort Yang, Eunmok
collection PubMed
description Object detection in pedestrian walkways is a crucial area of research that is widely used to improve the safety of pedestrians. It is not only challenging but also a tedious process to manually examine the labeling of abnormal actions, owing to its broad applications in video surveillance systems and the larger number of videos captured. Thus, an automatic surveillance system that identifies the anomalies has become indispensable for computer vision (CV) researcher workers. The recent advancements in deep learning (DL) algorithms have attracted wide attention for CV processes such as object detection and object classification based on supervised learning that requires labels. The current research study designs the bioinspired Garra rufa optimization-assisted deep learning model for object classification (BGRODL-OC) technique on pedestrian walkways. The objective of the BGRODL-OC technique is to recognize the presence of pedestrians and objects in the surveillance video. To achieve this goal, the BGRODL-OC technique primarily applies the GhostNet feature extractors to produce a set of feature vectors. In addition to this, the BGRODL-OC technique makes use of the GRO algorithm for hyperparameter tuning process. Finally, the object classification is performed via the attention-based long short-term memory (ALSTM) network. A wide range of experimental analysis was conducted to validate the superior performance of the BGRODL-OC technique. The experimental values established the superior performance of the BGRODL-OC algorithm over other existing approaches.
format Online
Article
Text
id pubmed-10669902
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-106699022023-11-11 Bioinspired Garra Rufa Optimization-Assisted Deep Learning Model for Object Classification on Pedestrian Walkways Yang, Eunmok Shankar, K. Kumar, Sachin Seo, Changho Biomimetics (Basel) Article Object detection in pedestrian walkways is a crucial area of research that is widely used to improve the safety of pedestrians. It is not only challenging but also a tedious process to manually examine the labeling of abnormal actions, owing to its broad applications in video surveillance systems and the larger number of videos captured. Thus, an automatic surveillance system that identifies the anomalies has become indispensable for computer vision (CV) researcher workers. The recent advancements in deep learning (DL) algorithms have attracted wide attention for CV processes such as object detection and object classification based on supervised learning that requires labels. The current research study designs the bioinspired Garra rufa optimization-assisted deep learning model for object classification (BGRODL-OC) technique on pedestrian walkways. The objective of the BGRODL-OC technique is to recognize the presence of pedestrians and objects in the surveillance video. To achieve this goal, the BGRODL-OC technique primarily applies the GhostNet feature extractors to produce a set of feature vectors. In addition to this, the BGRODL-OC technique makes use of the GRO algorithm for hyperparameter tuning process. Finally, the object classification is performed via the attention-based long short-term memory (ALSTM) network. A wide range of experimental analysis was conducted to validate the superior performance of the BGRODL-OC technique. The experimental values established the superior performance of the BGRODL-OC algorithm over other existing approaches. MDPI 2023-11-11 /pmc/articles/PMC10669902/ /pubmed/37999182 http://dx.doi.org/10.3390/biomimetics8070541 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 Article
Yang, Eunmok
Shankar, K.
Kumar, Sachin
Seo, Changho
Bioinspired Garra Rufa Optimization-Assisted Deep Learning Model for Object Classification on Pedestrian Walkways
title Bioinspired Garra Rufa Optimization-Assisted Deep Learning Model for Object Classification on Pedestrian Walkways
title_full Bioinspired Garra Rufa Optimization-Assisted Deep Learning Model for Object Classification on Pedestrian Walkways
title_fullStr Bioinspired Garra Rufa Optimization-Assisted Deep Learning Model for Object Classification on Pedestrian Walkways
title_full_unstemmed Bioinspired Garra Rufa Optimization-Assisted Deep Learning Model for Object Classification on Pedestrian Walkways
title_short Bioinspired Garra Rufa Optimization-Assisted Deep Learning Model for Object Classification on Pedestrian Walkways
title_sort bioinspired garra rufa optimization-assisted deep learning model for object classification on pedestrian walkways
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669902/
https://www.ncbi.nlm.nih.gov/pubmed/37999182
http://dx.doi.org/10.3390/biomimetics8070541
work_keys_str_mv AT yangeunmok bioinspiredgarrarufaoptimizationassisteddeeplearningmodelforobjectclassificationonpedestrianwalkways
AT shankark bioinspiredgarrarufaoptimizationassisteddeeplearningmodelforobjectclassificationonpedestrianwalkways
AT kumarsachin bioinspiredgarrarufaoptimizationassisteddeeplearningmodelforobjectclassificationonpedestrianwalkways
AT seochangho bioinspiredgarrarufaoptimizationassisteddeeplearningmodelforobjectclassificationonpedestrianwalkways