Object Detection in Omnidirectional Images
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.8/5591 |
Resumo: | Nowadays, computer vision (CV) is widely used to solve real-world problems, which pose increasingly higher challenges. In this context, the use of omnidirectional video in a growing number of applications, along with the fast development of Deep Learning (DL) algorithms for object detection, drives the need for further research to improve existing methods originally developed for conventional 2D planar images. However, the geometric distortion that common sphere-to-plane projections produce, mostly visible in objects near the poles, in addition to the lack of omnidirectional open-source labeled image datasets has made an accurate spherical image-based object detection algorithm a hard goal to achieve. This work is a contribution to develop datasets and machine learning models particularly suited for omnidirectional images, represented in planar format through the well-known Equirectangular Projection (ERP). To this aim, DL methods are explored to improve the detection of visual objects in omnidirectional images, by considering the inherent distortions of ERP. An experimental study was, firstly, carried out to find out whether the error rate and type of detection errors were related to the characteristics of ERP images. Such study revealed that the error rate of object detection using existing DL models with ERP images, actually, depends on the object spherical location in the image. Then, based on such findings, a new object detection framework is proposed to obtain a uniform error rate across the whole spherical image regions. The results show that the pre and post-processing stages of the implemented framework effectively contribute to reducing the performance dependency on the image region, evaluated by the above-mentioned metric. |
id |
RCAP_0a8508e833376817ab58431f08c0448f |
---|---|
oai_identifier_str |
oai:iconline.ipleiria.pt:10400.8/5591 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Object Detection in Omnidirectional ImagesComputer VisionDeep LearningObject DetectionEquirectangular ProjectionOmnidirectional imagesDomínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaNowadays, computer vision (CV) is widely used to solve real-world problems, which pose increasingly higher challenges. In this context, the use of omnidirectional video in a growing number of applications, along with the fast development of Deep Learning (DL) algorithms for object detection, drives the need for further research to improve existing methods originally developed for conventional 2D planar images. However, the geometric distortion that common sphere-to-plane projections produce, mostly visible in objects near the poles, in addition to the lack of omnidirectional open-source labeled image datasets has made an accurate spherical image-based object detection algorithm a hard goal to achieve. This work is a contribution to develop datasets and machine learning models particularly suited for omnidirectional images, represented in planar format through the well-known Equirectangular Projection (ERP). To this aim, DL methods are explored to improve the detection of visual objects in omnidirectional images, by considering the inherent distortions of ERP. An experimental study was, firstly, carried out to find out whether the error rate and type of detection errors were related to the characteristics of ERP images. Such study revealed that the error rate of object detection using existing DL models with ERP images, actually, depends on the object spherical location in the image. Then, based on such findings, a new object detection framework is proposed to obtain a uniform error rate across the whole spherical image regions. The results show that the pre and post-processing stages of the implemented framework effectively contribute to reducing the performance dependency on the image region, evaluated by the above-mentioned metric.Costa, Joana Madeira MartinsAssunção, Pedro António Amado deSilva, Catarina Helena Branco Simões daIC-OnlineHenriques, Francisco António Agostinho2021-03-31T09:40:50Z2021-01-262021-01-26T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.8/5591TID:202689514enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-17T15:51:26Zoai:iconline.ipleiria.pt:10400.8/5591Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:49:03.658213Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Object Detection in Omnidirectional Images |
title |
Object Detection in Omnidirectional Images |
spellingShingle |
Object Detection in Omnidirectional Images Henriques, Francisco António Agostinho Computer Vision Deep Learning Object Detection Equirectangular Projection Omnidirectional images Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
title_short |
Object Detection in Omnidirectional Images |
title_full |
Object Detection in Omnidirectional Images |
title_fullStr |
Object Detection in Omnidirectional Images |
title_full_unstemmed |
Object Detection in Omnidirectional Images |
title_sort |
Object Detection in Omnidirectional Images |
author |
Henriques, Francisco António Agostinho |
author_facet |
Henriques, Francisco António Agostinho |
author_role |
author |
dc.contributor.none.fl_str_mv |
Costa, Joana Madeira Martins Assunção, Pedro António Amado de Silva, Catarina Helena Branco Simões da IC-Online |
dc.contributor.author.fl_str_mv |
Henriques, Francisco António Agostinho |
dc.subject.por.fl_str_mv |
Computer Vision Deep Learning Object Detection Equirectangular Projection Omnidirectional images Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
topic |
Computer Vision Deep Learning Object Detection Equirectangular Projection Omnidirectional images Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
description |
Nowadays, computer vision (CV) is widely used to solve real-world problems, which pose increasingly higher challenges. In this context, the use of omnidirectional video in a growing number of applications, along with the fast development of Deep Learning (DL) algorithms for object detection, drives the need for further research to improve existing methods originally developed for conventional 2D planar images. However, the geometric distortion that common sphere-to-plane projections produce, mostly visible in objects near the poles, in addition to the lack of omnidirectional open-source labeled image datasets has made an accurate spherical image-based object detection algorithm a hard goal to achieve. This work is a contribution to develop datasets and machine learning models particularly suited for omnidirectional images, represented in planar format through the well-known Equirectangular Projection (ERP). To this aim, DL methods are explored to improve the detection of visual objects in omnidirectional images, by considering the inherent distortions of ERP. An experimental study was, firstly, carried out to find out whether the error rate and type of detection errors were related to the characteristics of ERP images. Such study revealed that the error rate of object detection using existing DL models with ERP images, actually, depends on the object spherical location in the image. Then, based on such findings, a new object detection framework is proposed to obtain a uniform error rate across the whole spherical image regions. The results show that the pre and post-processing stages of the implemented framework effectively contribute to reducing the performance dependency on the image region, evaluated by the above-mentioned metric. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-03-31T09:40:50Z 2021-01-26 2021-01-26T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.8/5591 TID:202689514 |
url |
http://hdl.handle.net/10400.8/5591 |
identifier_str_mv |
TID:202689514 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799136983600070656 |