Multi-camera person re-identification based on trajectory data
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/29667 |
Resumo: | This study presents a trajectory-based person re-identification algorithm, embedded in a tool to detect and track customers present in a large retail store, in a multi-camera environment. The customer trajectory data are obtained from video surveillance images captured by multiple cameras, and customers are detected and tracked along the frames that compose the videos. Due to the characteristics of a multi-camera environment or the occurrence of occlusions, caused by objects such as shelves or counters, different identifiers are assigned to each person when, in fact, they should be identified with a unique identifier. Thus, the proposed tool tries to solve this problem in a scenario where there are constraints in using images of people due to data privacy concerns. The results show that our method was able to correctly re-identify the customers present in the store with a re-identification rate of 82%. |
id |
RCAP_1746d03d9bcc5306519280f3dd148b44 |
---|---|
oai_identifier_str |
oai:repositorio.iscte-iul.pt:10071/29667 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Multi-camera person re-identification based on trajectory dataPerson re-identificationTrajectoryMulti-cameraObject detectionObject trackingComputer visionThis study presents a trajectory-based person re-identification algorithm, embedded in a tool to detect and track customers present in a large retail store, in a multi-camera environment. The customer trajectory data are obtained from video surveillance images captured by multiple cameras, and customers are detected and tracked along the frames that compose the videos. Due to the characteristics of a multi-camera environment or the occurrence of occlusions, caused by objects such as shelves or counters, different identifiers are assigned to each person when, in fact, they should be identified with a unique identifier. Thus, the proposed tool tries to solve this problem in a scenario where there are constraints in using images of people due to data privacy concerns. The results show that our method was able to correctly re-identify the customers present in the store with a re-identification rate of 82%.MDPI2023-11-20T13:06:09Z2023-01-01T00:00:00Z20232023-11-20T13:05:05Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/29667eng2076-341710.3390/app132011578Mendes, D.Correia, S.Jorge, P.Brandão, T.Arriaga, P.Nunes, L.info: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:RCAAP2023-11-26T01:17:29Zoai:repositorio.iscte-iul.pt:10071/29667Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:19:43.731844Repositório 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 |
Multi-camera person re-identification based on trajectory data |
title |
Multi-camera person re-identification based on trajectory data |
spellingShingle |
Multi-camera person re-identification based on trajectory data Mendes, D. Person re-identification Trajectory Multi-camera Object detection Object tracking Computer vision |
title_short |
Multi-camera person re-identification based on trajectory data |
title_full |
Multi-camera person re-identification based on trajectory data |
title_fullStr |
Multi-camera person re-identification based on trajectory data |
title_full_unstemmed |
Multi-camera person re-identification based on trajectory data |
title_sort |
Multi-camera person re-identification based on trajectory data |
author |
Mendes, D. |
author_facet |
Mendes, D. Correia, S. Jorge, P. Brandão, T. Arriaga, P. Nunes, L. |
author_role |
author |
author2 |
Correia, S. Jorge, P. Brandão, T. Arriaga, P. Nunes, L. |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Mendes, D. Correia, S. Jorge, P. Brandão, T. Arriaga, P. Nunes, L. |
dc.subject.por.fl_str_mv |
Person re-identification Trajectory Multi-camera Object detection Object tracking Computer vision |
topic |
Person re-identification Trajectory Multi-camera Object detection Object tracking Computer vision |
description |
This study presents a trajectory-based person re-identification algorithm, embedded in a tool to detect and track customers present in a large retail store, in a multi-camera environment. The customer trajectory data are obtained from video surveillance images captured by multiple cameras, and customers are detected and tracked along the frames that compose the videos. Due to the characteristics of a multi-camera environment or the occurrence of occlusions, caused by objects such as shelves or counters, different identifiers are assigned to each person when, in fact, they should be identified with a unique identifier. Thus, the proposed tool tries to solve this problem in a scenario where there are constraints in using images of people due to data privacy concerns. The results show that our method was able to correctly re-identify the customers present in the store with a re-identification rate of 82%. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-11-20T13:06:09Z 2023-01-01T00:00:00Z 2023 2023-11-20T13:05:05Z |
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.uri.fl_str_mv |
http://hdl.handle.net/10071/29667 |
url |
http://hdl.handle.net/10071/29667 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2076-3417 10.3390/app132011578 |
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.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
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_ |
1799135496257929216 |