Long-term person reidentification: challenges and outlook

Manhães, Anderson; Matos, Gabriel; Cardoso, Douglas O.; Pinto, Milena F.; Colares, Jeferson; Leitão, Paulo; Brandão, Diego; Haddad, Diego

Publication

Long-term person reidentification: challenges and outlook

2022Conference paper

dc.contributor.author	Manhães, Anderson
dc.contributor.author	Matos, Gabriel
dc.contributor.author	Cardoso, Douglas O.
dc.contributor.author	Pinto, Milena F.
dc.contributor.author	Colares, Jeferson
dc.contributor.author	Leitão, Paulo
dc.contributor.author	Brandão, Diego
dc.contributor.author	Haddad, Diego
dc.date.accessioned	2023-03-06T15:36:39Z
dc.date.available	2023-03-06T15:36:39Z
dc.date.issued	2022
dc.description.abstract	Person reidentification, i.e., retrieving a person of interest across several non-overlapping cameras, is a task that is far from trivial. Despite its great commercial value and wide range of applications (e.g., surveillance, intelligent environments, forensics, service robotics, marketing), it remains unsolved, even when the individuals do not change clothes during the recognition period. This paper provides an outlook on long-term person reidentification, an emerging research topic regarding when consecutive acquisitions of an individual can be found apart for days or even months, making such a task even more challenging. A long-term reidentification system using face recognition is presented to emphasize current techniques’ limitations.	pt_PT
dc.description.sponsorship	FCT (Fundação para a Ciência e a Tecnologia), under the project UIDB/05567/2020.
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Manhães, Anderson; Matos, Gabriel; Cardoso, Douglas O.; Pinto, Milena F.; Colares, Jeferson; Leitão, Paulo; Brandão, Diego; Haddad, Diego (2022). Long-term person reidentification: challenges and outlook. In 2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022. Bragança. 1754, p. 357-372	pt_PT
dc.identifier.doi	10.1007/978-3-031-23236-7_25	pt_PT
dc.identifier.uri	http://hdl.handle.net/10198/27494
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	Springer	pt_PT
dc.relation	Smart Cities Research Center
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Long-term person ReID	pt_PT
dc.subject	Deep learning	pt_PT
dc.subject	Computer vision	pt_PT
dc.subject	Multimodal retrieval	pt_PT
dc.title	Long-term person reidentification: challenges and outlook	pt_PT
dc.type	conference paper
dspace.entity.type	Publication
oaire.awardTitle	Smart Cities Research Center
oaire.awardURI	info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05567%2F2020/PT
oaire.citation.conferencePlace	Bragança	pt_PT
oaire.citation.endPage	372	pt_PT
oaire.citation.startPage	357	pt_PT
oaire.citation.title	2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022	pt_PT
oaire.citation.volume	1754	pt_PT
oaire.fundingStream	6817 - DCRRNI ID
person.familyName	Leitão
person.givenName	Paulo
person.identifier	A-8390-2011
person.identifier.ciencia-id	8316-8F13-DA71
person.identifier.orcid	0000-0002-2151-7944
person.identifier.scopus-author-id	35584388900
project.funder.identifier	http://doi.org/10.13039/501100001871
project.funder.name	Fundação para a Ciência e a Tecnologia
rcaap.rights	restrictedAccess	pt_PT
rcaap.type	conferenceObject	pt_PT
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