000169179 001__ 169179
000169179 005__ 20260223164758.0
000169179 0247_ $$2doi$$a10.3390/s23052662
000169179 0248_ $$2sideral$$a132966
000169179 037__ $$aART-2023-132966
000169179 041__ $$aeng
000169179 100__ $$aCasao, Sara$$uUniversidad de Zaragoza
000169179 245__ $$aA self-adaptive gallery construction method for open-world person re-identification
000169179 260__ $$c2023
000169179 5060_ $$aAccess copy available to the general public$$fUnrestricted
000169179 5203_ $$aPerson re-identification, or simply re-id, is the task of identifying again a person who has been seen in the past by a perception system. Multiple robotic applications, such as tracking or navigate-and-seek, use re-identification systems to perform their tasks. To solve the re-id problem, a common practice consists in using a gallery with relevant information about the people already observed. The construction of this gallery is a costly process, typically performed offline and only once because of the problems associated with labeling and storing new data as they arrive in the system. The resulting galleries from this process are static and do not acquire new knowledge from the scene, which is a limitation of the current re-id systems to work for open-world applications. Different from previous work, we overcome this limitation by presenting an unsupervised approach to automatically identify new people and incrementally build a gallery for open-world re-id that adapts prior knowledge with new information on a continuous basis. Our approach performs a comparison between the current person models and new unlabeled data to dynamically expand the gallery with new identities. We process the incoming information to maintain a small representative model of each person by exploiting concepts of information theory. The uncertainty and diversity of the new samples are analyzed to define which ones should be incorporated into the gallery. Experimental evaluation in challenging benchmarks includes an ablation study of the proposed framework, the assessment of different data selection algorithms that demonstrate the benefits of our approach, and a comparative analysis of the obtained results with other unsupervised and semi-supervised re-id methods.
000169179 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T45-20R$$9info:eu-repo/grantAgreement/ES/MICINN/PID2021-125514NB-I00
000169179 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000169179 590__ $$a3.4$$b2023
000169179 591__ $$aCHEMISTRY, ANALYTICAL$$b34 / 106 = 0.321$$c2023$$dQ2$$eT1
000169179 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b24 / 76 = 0.316$$c2023$$dQ2$$eT1
000169179 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b122 / 353 = 0.346$$c2023$$dQ2$$eT2
000169179 592__ $$a0.786$$b2023
000169179 593__ $$aInstrumentation$$c2023$$dQ1
000169179 593__ $$aAnalytical Chemistry$$c2023$$dQ1
000169179 593__ $$aAtomic and Molecular Physics, and Optics$$c2023$$dQ1
000169179 593__ $$aInformation Systems$$c2023$$dQ2
000169179 593__ $$aMedicine (miscellaneous)$$c2023$$dQ2
000169179 593__ $$aBiochemistry$$c2023$$dQ2
000169179 593__ $$aElectrical and Electronic Engineering$$c2023$$dQ2
000169179 594__ $$a7.3$$b2023
000169179 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000169179 700__ $$0(orcid)0000-0002-3567-3294$$aAzagra, Pablo$$uUniversidad de Zaragoza
000169179 700__ $$0(orcid)0000-0002-7580-9037$$aMurillo, Ana C.$$uUniversidad de Zaragoza
000169179 700__ $$0(orcid)0000-0002-5176-3767$$aMontijano, Eduardo$$uUniversidad de Zaragoza
000169179 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000169179 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000169179 773__ $$g23, 5 (2023), 2662 [17 pp.]$$pSensors$$tSensors$$x1424-8220
000169179 8564_ $$s3607656$$uhttps://zaguan.unizar.es/record/169179/files/texto_completo.pdf$$yVersión publicada
000169179 8564_ $$s2723201$$uhttps://zaguan.unizar.es/record/169179/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000169179 909CO $$ooai:zaguan.unizar.es:169179$$particulos$$pdriver
000169179 951__ $$a2026-02-23-14:53:55
000169179 980__ $$aARTICLE