People detection with omnidirectional cameras using aspatial grid ofdeep learning foveatic classifiers

A novel deep-learning people detection algorithm using omnidirectional cameras is presented, which only requires point-based annotations, unlike most of the prominent works that require bounding box annotations. Thus, the effort of manually annotating the needed training databases is significantly r...

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Detalles Bibliográficos
Autores: Fuertes, Daniel, Blanco, Carlos R. del, Jaureguizar, Fernando, Carballeira López, Pablo
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/711149
Acceso en línea:http://hdl.handle.net/10486/711149
https://dx.doi.org/10.1016/j.dsp.2022.103473
Access Level:acceso abierto
Palabra clave:Spatial grid
Deep learning
Omnidirectional cameras
People detection
Point based annotations
Telecomunicaciones
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spelling People detection with omnidirectional cameras using aspatial grid ofdeep learning foveatic classifiersFuertes, DanielBlanco, Carlos R. delJaureguizar, FernandoCarballeira López, PabloSpatial gridDeep learningOmnidirectional camerasPeople detectionPoint based annotationsTelecomunicacionesA novel deep-learning people detection algorithm using omnidirectional cameras is presented, which only requires point-based annotations, unlike most of the prominent works that require bounding box annotations. Thus, the effort of manually annotating the needed training databases is significantly reduced, allowing a faster system deployment. The algorithm is based on a novel deep neural network architecture that implements the concept of Grid of Spatial-Aware Classifiers, but allowing end-to-end training that improves the performance of the whole system. The designed algorithm satisfactorily handles the severe geometric distortions of the omnidirectional images, which typically degrades the performance of state-of-the-art detectors, without requiring any camera calibration. The algorithm has been evaluated in well-known omnidirectional image databases (PIROPO, BOMNI, and MW-18Mar) and compared with several works of the state of the art.This work has been partially supported by project PID2020115132RB (SARAOS) funded by MCIN/AEI/10.13039/501100011033 of the Spanish Government.ElsevierDepartamento de Tecnología Electrónica y de las ComunicacionesEscuela Politécnica Superior20222022-02-18research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/711149https://dx.doi.org/10.1016/j.dsp.2022.103473reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7111492026-06-23T12:46:27Z
dc.title.none.fl_str_mv People detection with omnidirectional cameras using aspatial grid ofdeep learning foveatic classifiers
title People detection with omnidirectional cameras using aspatial grid ofdeep learning foveatic classifiers
spellingShingle People detection with omnidirectional cameras using aspatial grid ofdeep learning foveatic classifiers
Fuertes, Daniel
Spatial grid
Deep learning
Omnidirectional cameras
People detection
Point based annotations
Telecomunicaciones
title_short People detection with omnidirectional cameras using aspatial grid ofdeep learning foveatic classifiers
title_full People detection with omnidirectional cameras using aspatial grid ofdeep learning foveatic classifiers
title_fullStr People detection with omnidirectional cameras using aspatial grid ofdeep learning foveatic classifiers
title_full_unstemmed People detection with omnidirectional cameras using aspatial grid ofdeep learning foveatic classifiers
title_sort People detection with omnidirectional cameras using aspatial grid ofdeep learning foveatic classifiers
dc.creator.none.fl_str_mv Fuertes, Daniel
Blanco, Carlos R. del
Jaureguizar, Fernando
Carballeira López, Pablo
author Fuertes, Daniel
author_facet Fuertes, Daniel
Blanco, Carlos R. del
Jaureguizar, Fernando
Carballeira López, Pablo
author_role author
author2 Blanco, Carlos R. del
Jaureguizar, Fernando
Carballeira López, Pablo
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Tecnología Electrónica y de las Comunicaciones
Escuela Politécnica Superior
dc.subject.none.fl_str_mv Spatial grid
Deep learning
Omnidirectional cameras
People detection
Point based annotations
Telecomunicaciones
topic Spatial grid
Deep learning
Omnidirectional cameras
People detection
Point based annotations
Telecomunicaciones
description A novel deep-learning people detection algorithm using omnidirectional cameras is presented, which only requires point-based annotations, unlike most of the prominent works that require bounding box annotations. Thus, the effort of manually annotating the needed training databases is significantly reduced, allowing a faster system deployment. The algorithm is based on a novel deep neural network architecture that implements the concept of Grid of Spatial-Aware Classifiers, but allowing end-to-end training that improves the performance of the whole system. The designed algorithm satisfactorily handles the severe geometric distortions of the omnidirectional images, which typically degrades the performance of state-of-the-art detectors, without requiring any camera calibration. The algorithm has been evaluated in well-known omnidirectional image databases (PIROPO, BOMNI, and MW-18Mar) and compared with several works of the state of the art.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-02-18
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/711149
https://dx.doi.org/10.1016/j.dsp.2022.103473
url http://hdl.handle.net/10486/711149
https://dx.doi.org/10.1016/j.dsp.2022.103473
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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