Scale Invariant Mask R-CNN for Pedestrian Detection

Pedestrian detection is a challenging and active research area in computer vision. Recognizing pedestrianshelps in various utility applications such as event detection in overcrowded areas, gender, and gaitclassification, etc. In this domain, the most recent research is based on instance segmentatio...

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Autores: Gawande, Ujwalla H., Hajari, Kamal Omprakash|||0000-0002-4959-8117, Golhar, Yogesh|||0000-0002-6817-3552
Formato: artículo
Fecha de publicación:2020
País:España
Recursos:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:233664
Acesso em linha:https://ddd.uab.cat/record/233664
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1278
Access Level:acceso abierto
Palavra-chave:Convolutional neural network
Instance segmentation
Pedestrian Detection
Mask R-CNN
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spelling Scale Invariant Mask R-CNN for Pedestrian DetectionGawande, Ujwalla H.Hajari, Kamal Omprakash|||0000-0002-4959-8117Golhar, Yogesh|||0000-0002-6817-3552Convolutional neural networkInstance segmentationPedestrian DetectionMask R-CNNPedestrian detection is a challenging and active research area in computer vision. Recognizing pedestrianshelps in various utility applications such as event detection in overcrowded areas, gender, and gaitclassification, etc. In this domain, the most recent research is based on instance segmentation using MaskR-CNN. Most of the pedestrian detection method uses a feature of different body portions for identifying aperson. This feature-based approach is not efficient enough to differentiate pedestrians in real-time, wherethe background changing. In this paper, a combined approach of scale-invariant feature map generationfor detecting a small pedestrian and Mask R-CNN has been proposed for multiple pedestrian detection toovercome this drawback. The new database was created by recording the behavior of the student at theprominent places of the engineering institute. This database is comparatively new for pedestrian detectionin the academic environment. The proposed Scale-invariant Mask R-CNN has been tested on the newlycreated database and has been compared with the Caltech [1], INRIA [2], MS COCO [3], ETH [4], andKITTI [5] database. The experimental result shows significant performance improvement in pedestrian detection as compared to the existing approaches of pedestrian detection and instance segmentation. Finally, we conclude and investigate the directions for future research. 22020-01-0120202020-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/233664https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1278reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2336642026-06-06T12:50:31Z
dc.title.none.fl_str_mv Scale Invariant Mask R-CNN for Pedestrian Detection
title Scale Invariant Mask R-CNN for Pedestrian Detection
spellingShingle Scale Invariant Mask R-CNN for Pedestrian Detection
Gawande, Ujwalla H.
Convolutional neural network
Instance segmentation
Pedestrian Detection
Mask R-CNN
title_short Scale Invariant Mask R-CNN for Pedestrian Detection
title_full Scale Invariant Mask R-CNN for Pedestrian Detection
title_fullStr Scale Invariant Mask R-CNN for Pedestrian Detection
title_full_unstemmed Scale Invariant Mask R-CNN for Pedestrian Detection
title_sort Scale Invariant Mask R-CNN for Pedestrian Detection
dc.creator.none.fl_str_mv Gawande, Ujwalla H.
Hajari, Kamal Omprakash|||0000-0002-4959-8117
Golhar, Yogesh|||0000-0002-6817-3552
author Gawande, Ujwalla H.
author_facet Gawande, Ujwalla H.
Hajari, Kamal Omprakash|||0000-0002-4959-8117
Golhar, Yogesh|||0000-0002-6817-3552
author_role author
author2 Hajari, Kamal Omprakash|||0000-0002-4959-8117
Golhar, Yogesh|||0000-0002-6817-3552
author2_role author
author
dc.subject.none.fl_str_mv Convolutional neural network
Instance segmentation
Pedestrian Detection
Mask R-CNN
topic Convolutional neural network
Instance segmentation
Pedestrian Detection
Mask R-CNN
description Pedestrian detection is a challenging and active research area in computer vision. Recognizing pedestrianshelps in various utility applications such as event detection in overcrowded areas, gender, and gaitclassification, etc. In this domain, the most recent research is based on instance segmentation using MaskR-CNN. Most of the pedestrian detection method uses a feature of different body portions for identifying aperson. This feature-based approach is not efficient enough to differentiate pedestrians in real-time, wherethe background changing. In this paper, a combined approach of scale-invariant feature map generationfor detecting a small pedestrian and Mask R-CNN has been proposed for multiple pedestrian detection toovercome this drawback. The new database was created by recording the behavior of the student at theprominent places of the engineering institute. This database is comparatively new for pedestrian detectionin the academic environment. The proposed Scale-invariant Mask R-CNN has been tested on the newlycreated database and has been compared with the Caltech [1], INRIA [2], MS COCO [3], ETH [4], andKITTI [5] database. The experimental result shows significant performance improvement in pedestrian detection as compared to the existing approaches of pedestrian detection and instance segmentation. Finally, we conclude and investigate the directions for future research.
publishDate 2020
dc.date.none.fl_str_mv 2
2020-01-01
2020
2020-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
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 https://ddd.uab.cat/record/233664
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1278
url https://ddd.uab.cat/record/233664
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1278
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
https://creativecommons.org/licenses/by-nc-nd/4.0/
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
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
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