Zebrafish tracking using YOLOv2 and Kalman filter
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRN |
Texto Completo: | https://repositorio.ufrn.br/handle/123456789/31459 |
Resumo: | Fish show rapid movements in various behavioral activities or associated with the presence of food. However, in periods of rapid movement, the rate at which occlusion occurs among the fish is quite high, causing inconsistency in the detection and tracking of fish, hindering the fish's identity and behavioral trajectory over a long period of time. Although some algorithms have been proposed to solve these problems, most of their applications were made in groups of fish that swim in shallow water and calm behavior, with few sudden movements. To solve these problems, a convolutional network of object recognition, YOLOv2, was used to delimit the region of the fish heads to optimize individual fish detection. In the tracking phase, the Kalman filter was used to estimate the best state of the fish's head position in each frame and, subsequently, the trajectories of each fish were connected among the frames. The results of the algorithm show adequate performances in the trajectories of groups of zebrafish that exhibited rapid movements |
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Barreiros, Marta de OliveiraDantas, Diego de OliveiraSilva, Luis Claudio de OliveiraRibeiro, Sidarta Tollendal GomesBarros Filho, Allan Kardec Duailibe2021-02-10T17:18:30Z2021-02-10T17:18:30Z2021-02-05BARREIROS, Marta de Oliveira; DANTAS, Diego de Oliveira; SILVA, Luís Claudio de Oliveira; RIBEIRO, Sidarta; BARROS, Allan Kardec. Zebrafish tracking using YOLOv2 and Kalman filter. Scientific Reports, [S.l.], v. 11, n. 1, p. 3219, fev. 2021. doi: http://dx.doi.org/10.1038/s41598-021-81997-9. Disponível em: https://www.nature.com/articles/s41598-021-81997-9. Acesso em: 10 fev. 2021.https://repositorio.ufrn.br/handle/123456789/3145910.1038/s41598-021-81997-9Springer Science and Business Media LLC.Attribution 3.0 Brazilhttp://creativecommons.org/licenses/by/3.0/br/info:eu-repo/semantics/openAccessZebrafishYOLOv2 networkKalman filterBehavior, animalZebrafish tracking using YOLOv2 and Kalman filterinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleFish show rapid movements in various behavioral activities or associated with the presence of food. However, in periods of rapid movement, the rate at which occlusion occurs among the fish is quite high, causing inconsistency in the detection and tracking of fish, hindering the fish's identity and behavioral trajectory over a long period of time. Although some algorithms have been proposed to solve these problems, most of their applications were made in groups of fish that swim in shallow water and calm behavior, with few sudden movements. To solve these problems, a convolutional network of object recognition, YOLOv2, was used to delimit the region of the fish heads to optimize individual fish detection. In the tracking phase, the Kalman filter was used to estimate the best state of the fish's head position in each frame and, subsequently, the trajectories of each fish were connected among the frames. The results of the algorithm show adequate performances in the trajectories of groups of zebrafish that exhibited rapid movementsengreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNORIGINALZebrafishTrackingYOLOv2_Ribeiro_2021.pdfZebrafishTrackingYOLOv2_Ribeiro_2021.pdfZebrafishTrackingYOLOv2_Ribeiro_2021application/pdf3087396https://repositorio.ufrn.br/bitstream/123456789/31459/1/ZebrafishTrackingYOLOv2_Ribeiro_2021.pdfe9de1f144b74094eec24abf0de3ab7a8MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8914https://repositorio.ufrn.br/bitstream/123456789/31459/2/license_rdf4d2950bda3d176f570a9f8b328dfbbefMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81484https://repositorio.ufrn.br/bitstream/123456789/31459/3/license.txte9597aa2854d128fd968be5edc8a28d9MD53TEXTZebrafishTrackingYOLOv2_Ribeiro_2021.pdf.txtZebrafishTrackingYOLOv2_Ribeiro_2021.pdf.txtExtracted texttext/plain53910https://repositorio.ufrn.br/bitstream/123456789/31459/4/ZebrafishTrackingYOLOv2_Ribeiro_2021.pdf.txt7b4f7643dd311ecb51b515b450777d38MD54THUMBNAILZebrafishTrackingYOLOv2_Ribeiro_2021.pdf.jpgZebrafishTrackingYOLOv2_Ribeiro_2021.pdf.jpgGenerated Thumbnailimage/jpeg1815https://repositorio.ufrn.br/bitstream/123456789/31459/5/ZebrafishTrackingYOLOv2_Ribeiro_2021.pdf.jpgcd63b59ee46cba69c5cf8ff960588f7aMD55123456789/314592021-02-14 05:48:39.941oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2021-02-14T08:48:39Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false |
dc.title.pt_BR.fl_str_mv |
Zebrafish tracking using YOLOv2 and Kalman filter |
title |
Zebrafish tracking using YOLOv2 and Kalman filter |
spellingShingle |
Zebrafish tracking using YOLOv2 and Kalman filter Barreiros, Marta de Oliveira Zebrafish YOLOv2 network Kalman filter Behavior, animal |
title_short |
Zebrafish tracking using YOLOv2 and Kalman filter |
title_full |
Zebrafish tracking using YOLOv2 and Kalman filter |
title_fullStr |
Zebrafish tracking using YOLOv2 and Kalman filter |
title_full_unstemmed |
Zebrafish tracking using YOLOv2 and Kalman filter |
title_sort |
Zebrafish tracking using YOLOv2 and Kalman filter |
author |
Barreiros, Marta de Oliveira |
author_facet |
Barreiros, Marta de Oliveira Dantas, Diego de Oliveira Silva, Luis Claudio de Oliveira Ribeiro, Sidarta Tollendal Gomes Barros Filho, Allan Kardec Duailibe |
author_role |
author |
author2 |
Dantas, Diego de Oliveira Silva, Luis Claudio de Oliveira Ribeiro, Sidarta Tollendal Gomes Barros Filho, Allan Kardec Duailibe |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Barreiros, Marta de Oliveira Dantas, Diego de Oliveira Silva, Luis Claudio de Oliveira Ribeiro, Sidarta Tollendal Gomes Barros Filho, Allan Kardec Duailibe |
dc.subject.por.fl_str_mv |
Zebrafish YOLOv2 network Kalman filter Behavior, animal |
topic |
Zebrafish YOLOv2 network Kalman filter Behavior, animal |
description |
Fish show rapid movements in various behavioral activities or associated with the presence of food. However, in periods of rapid movement, the rate at which occlusion occurs among the fish is quite high, causing inconsistency in the detection and tracking of fish, hindering the fish's identity and behavioral trajectory over a long period of time. Although some algorithms have been proposed to solve these problems, most of their applications were made in groups of fish that swim in shallow water and calm behavior, with few sudden movements. To solve these problems, a convolutional network of object recognition, YOLOv2, was used to delimit the region of the fish heads to optimize individual fish detection. In the tracking phase, the Kalman filter was used to estimate the best state of the fish's head position in each frame and, subsequently, the trajectories of each fish were connected among the frames. The results of the algorithm show adequate performances in the trajectories of groups of zebrafish that exhibited rapid movements |
publishDate |
2021 |
dc.date.accessioned.fl_str_mv |
2021-02-10T17:18:30Z |
dc.date.available.fl_str_mv |
2021-02-10T17:18:30Z |
dc.date.issued.fl_str_mv |
2021-02-05 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
BARREIROS, Marta de Oliveira; DANTAS, Diego de Oliveira; SILVA, Luís Claudio de Oliveira; RIBEIRO, Sidarta; BARROS, Allan Kardec. Zebrafish tracking using YOLOv2 and Kalman filter. Scientific Reports, [S.l.], v. 11, n. 1, p. 3219, fev. 2021. doi: http://dx.doi.org/10.1038/s41598-021-81997-9. Disponível em: https://www.nature.com/articles/s41598-021-81997-9. Acesso em: 10 fev. 2021. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufrn.br/handle/123456789/31459 |
dc.identifier.doi.none.fl_str_mv |
10.1038/s41598-021-81997-9 |
identifier_str_mv |
BARREIROS, Marta de Oliveira; DANTAS, Diego de Oliveira; SILVA, Luís Claudio de Oliveira; RIBEIRO, Sidarta; BARROS, Allan Kardec. Zebrafish tracking using YOLOv2 and Kalman filter. Scientific Reports, [S.l.], v. 11, n. 1, p. 3219, fev. 2021. doi: http://dx.doi.org/10.1038/s41598-021-81997-9. Disponível em: https://www.nature.com/articles/s41598-021-81997-9. Acesso em: 10 fev. 2021. 10.1038/s41598-021-81997-9 |
url |
https://repositorio.ufrn.br/handle/123456789/31459 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution 3.0 Brazil http://creativecommons.org/licenses/by/3.0/br/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution 3.0 Brazil http://creativecommons.org/licenses/by/3.0/br/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Springer Science and Business Media LLC. |
publisher.none.fl_str_mv |
Springer Science and Business Media LLC. |
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