Full Body Pedestrians Orientation Estimation using Machine Learning

Detalhes bibliográficos
Autor(a) principal: Zamir, Bukhtawar
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/107772
Resumo: Documentos apresentados no âmbito do reconhecimento de graus e diplomas estrangeiros
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spelling Full Body Pedestrians Orientation Estimation using Machine LearningClassificationPre-processingPedestrian OrientationSRGAN-VGG54VGG-19WOADocumentos apresentados no âmbito do reconhecimento de graus e diplomas estrangeirosIn human body orientation estimation (HBOE), body parts play a vital role to build the structure of humans. The estimation task has gotten tremendous attention in previous years due to its applications in almost every field of the real world like medical, surveillance, sports, augmented reality, animations, and many more. Although different approaches for HBOE have been presented, these methods still face obstacles like rapid variation in pose, different viewpoints, camera issues due to weather conditions, etc. The main purpose of this study is to deal with these addressed issues. So much work is being done in this field of research. In this research the dataset called big dataset for body orientation (BDBO) is used. The main purpose of the research is to give high estimation accuracy in short time. The research consists of a few core steps i.e. initial preprocessing, features extraction, features selection and classification. Additionally, the performance of the employed techniques is evaluated and studied to highlight the high results. The first step is image pre-processing in which image sharpening is performed and the resolution of images is enhanced with SRGAN-VGG54. For feature extraction the features from the dataset two CNNs are used in which one is VGG-19, a pre-trained network and the other is a proposed net called BlackNet. The main contribution of this research is BlackNet, which is used to extract the useful features form the dataset for better accuracy. After extracting the features from two CNNs, features are then fused. After feature fusion, features are then passed through the phase of feature selection. For this purpose Whale Optimization Algorithm (WOA) is used for extraction useful and optimal features from fused features. These optimal features are then passed to the state-of-the-art classifiers which are SVM and KNN. A detailed analysis of proposed methodology is given in the sections below to highlight the contribution of this research.2022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttp://hdl.handle.net/10316/107772http://hdl.handle.net/10316/107772engZamir, Bukhtawarinfo: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:RCAAP2023-08-01T10:04:18Zoai:estudogeral.uc.pt:10316/107772Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:24:04.846636Repositório 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 Full Body Pedestrians Orientation Estimation using Machine Learning
title Full Body Pedestrians Orientation Estimation using Machine Learning
spellingShingle Full Body Pedestrians Orientation Estimation using Machine Learning
Zamir, Bukhtawar
Classification
Pre-processing
Pedestrian Orientation
SRGAN-VGG54
VGG-19
WOA
title_short Full Body Pedestrians Orientation Estimation using Machine Learning
title_full Full Body Pedestrians Orientation Estimation using Machine Learning
title_fullStr Full Body Pedestrians Orientation Estimation using Machine Learning
title_full_unstemmed Full Body Pedestrians Orientation Estimation using Machine Learning
title_sort Full Body Pedestrians Orientation Estimation using Machine Learning
author Zamir, Bukhtawar
author_facet Zamir, Bukhtawar
author_role author
dc.contributor.author.fl_str_mv Zamir, Bukhtawar
dc.subject.por.fl_str_mv Classification
Pre-processing
Pedestrian Orientation
SRGAN-VGG54
VGG-19
WOA
topic Classification
Pre-processing
Pedestrian Orientation
SRGAN-VGG54
VGG-19
WOA
description Documentos apresentados no âmbito do reconhecimento de graus e diplomas estrangeiros
publishDate 2022
dc.date.none.fl_str_mv 2022
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/10316/107772
http://hdl.handle.net/10316/107772
url http://hdl.handle.net/10316/107772
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.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
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