Robust pedestrian detection and path prediction using mmproved YOLOv5

In vision-based surveillance systems, pedestrian recognition and path prediction are critical concerns. Advanced computer vision applications, on the other hand, confront numerous challenges due to differences in pedestrian postures and scales, backdrops, and occlusion. To tackle these challenges, w...

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Detalles Bibliográficos
Autores: Hajari, Kamal Omprakash|||0000-0002-4959-8117, Gawande, Ujwalla, Golhar, Yogesh|||0000-0002-6817-3552
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:265978
Acceso en línea:https://ddd.uab.cat/record/265978
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1538
Access Level:acceso abierto
Palabra clave:CNN
Deep learning
Pedestrian detection
Tracking
Path prediction
Computer vision
Yolov5
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oai_identifier_str oai:ddd.uab.cat:265978
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spelling Robust pedestrian detection and path prediction using mmproved YOLOv5Hajari, Kamal Omprakash|||0000-0002-4959-8117Gawande, UjwallaGolhar, Yogesh|||0000-0002-6817-3552CNNDeep learningPedestrian detectionTrackingPath predictionComputer visionYolov5In vision-based surveillance systems, pedestrian recognition and path prediction are critical concerns. Advanced computer vision applications, on the other hand, confront numerous challenges due to differences in pedestrian postures and scales, backdrops, and occlusion. To tackle these challenges, we present a YOLOv5-based deep learning-based pedestrian recognition and path prediction method. The updated YOLOv5 model was first used to detect pedestrians of various sizes and proportions. The proposed path prediction method is then used to estimate the pedestrian's path based on motion data. The suggested method deals with partial occlusion circumstances to reduce object occlusion-induced progression and loss, and links recognition results with motion attributes. After then, the path prediction algorithm uses motion and directional data to estimate the pedestrian movement's direction. The proposed method outperforms the existing methods, according to the results of the experiments. Finally, we come to a conclusion and look into future study. 22022-01-0120222022-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/265978https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1538reponame: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:2659782026-06-06T12:50:31Z
dc.title.none.fl_str_mv Robust pedestrian detection and path prediction using mmproved YOLOv5
title Robust pedestrian detection and path prediction using mmproved YOLOv5
spellingShingle Robust pedestrian detection and path prediction using mmproved YOLOv5
Hajari, Kamal Omprakash|||0000-0002-4959-8117
CNN
Deep learning
Pedestrian detection
Tracking
Path prediction
Computer vision
Yolov5
title_short Robust pedestrian detection and path prediction using mmproved YOLOv5
title_full Robust pedestrian detection and path prediction using mmproved YOLOv5
title_fullStr Robust pedestrian detection and path prediction using mmproved YOLOv5
title_full_unstemmed Robust pedestrian detection and path prediction using mmproved YOLOv5
title_sort Robust pedestrian detection and path prediction using mmproved YOLOv5
dc.creator.none.fl_str_mv Hajari, Kamal Omprakash|||0000-0002-4959-8117
Gawande, Ujwalla
Golhar, Yogesh|||0000-0002-6817-3552
author Hajari, Kamal Omprakash|||0000-0002-4959-8117
author_facet Hajari, Kamal Omprakash|||0000-0002-4959-8117
Gawande, Ujwalla
Golhar, Yogesh|||0000-0002-6817-3552
author_role author
author2 Gawande, Ujwalla
Golhar, Yogesh|||0000-0002-6817-3552
author2_role author
author
dc.subject.none.fl_str_mv CNN
Deep learning
Pedestrian detection
Tracking
Path prediction
Computer vision
Yolov5
topic CNN
Deep learning
Pedestrian detection
Tracking
Path prediction
Computer vision
Yolov5
description In vision-based surveillance systems, pedestrian recognition and path prediction are critical concerns. Advanced computer vision applications, on the other hand, confront numerous challenges due to differences in pedestrian postures and scales, backdrops, and occlusion. To tackle these challenges, we present a YOLOv5-based deep learning-based pedestrian recognition and path prediction method. The updated YOLOv5 model was first used to detect pedestrians of various sizes and proportions. The proposed path prediction method is then used to estimate the pedestrian's path based on motion data. The suggested method deals with partial occlusion circumstances to reduce object occlusion-induced progression and loss, and links recognition results with motion attributes. After then, the path prediction algorithm uses motion and directional data to estimate the pedestrian movement's direction. The proposed method outperforms the existing methods, according to the results of the experiments. Finally, we come to a conclusion and look into future study.
publishDate 2022
dc.date.none.fl_str_mv 2
2022-01-01
2022
2022-01-01
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/265978
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1538
url https://ddd.uab.cat/record/265978
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1538
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
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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