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dc.contributor.authorKuncheva, Ludmila I. .
dc.contributor.authorGarrido Labrador, José Luis 
dc.contributor.authorRamos Pérez, Ismael 
dc.contributor.authorHennessey, Samuel L.
dc.contributor.authorRodríguez Diez, Juan José 
dc.date.accessioned2023-03-14T11:13:26Z
dc.date.available2023-03-14T11:13:26Z
dc.date.issued2023-05
dc.identifier.issn1574-9541
dc.identifier.urihttp://hdl.handle.net/10259/7541
dc.description.abstractIn the face of the global concern about climate change and endangered ecosystems, monitoring individual animals is of paramount importance. Computer vision methods for animal recognition and re-identification from video or image collections are a modern alternative to more traditional but intrusive methods such as tagging or branding. While there are many studies reporting results on various animal re-identification databases, there is a notable lack of comparative studies between different classification methods. In this paper we offer a comparison of 25 classification methods including linear, non-linear and ensemble models, as well as deep learning networks. Since the animal databases are vastly different in characteristics and difficulty, we propose an experimental protocol that can be applied to a chosen data collections. We use a publicly available database of five video clips, each containing multiple identities (9 to 27), where the animals are typically present as a group in each video frame. Our experiment involves five data representations: colour, shape, texture, and two feature spaces extracted by deep learning. In our experiments, simpler models (linear classifiers) and just colour feature space gave the best classification accuracy, demonstrating the importance of running a comparative study before resorting to complex, time-consuming, and potentially less robust methods.en
dc.description.sponsorshipThis work is supported by the UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC), funded by grant EP/S023992/1. This work is also supported by the Junta de Castilla León under project BU055P20 (JCyL/FEDER, UE), and the Ministry of Science and Innovation under project PID2020-119894 GB-I00 co-financed through European Union FEDER funds. J.L. Garrido-Labrador is supported through Consejería de Educación of the Junta de Castilla y León and the European Social Fund through a pre-doctoral grant (EDU/875/2021). I. Ramos-Perez is supported by the predoctoral grant (BDNS 510149) awarded by the Universidad de Burgos, Spain. J.J. Rodríguez was supported by mobility grant PRX21/00638 of the Spanish Ministry of Universities.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofEcological Informatics. 2023, V. 74, 101994en
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAnimal re-identificationen
dc.subjectComputer visionen
dc.subjectClassificationen
dc.subjectConvolutional networksen
dc.subjectComparative studyen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.subject.otherBiologíaes
dc.subject.otherBiologyen
dc.titleAn experiment on animal re-identification from videoen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccesses
dc.relation.publisherversionhttps://doi.org/10.1016/j.ecoinf.2023.101994es
dc.identifier.doi10.1016/j.ecoinf.2023.101994
dc.relation.projectIDinfo:eu-repo/grantAgreement/UKRI//EP%2FS023992%2F1/GB/es
dc.relation.projectIDinfo:eu-repo/grantAgreement/Junta de Castilla y León//BU055P20//Métodos y Aplicaciones Industriales del Aprendizaje Semisupervisadoes
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119894GB-I00/ES/APRENDIZAJE AUTOMATICO CON DATOS ESCASAMENTE ETIQUETADOS PARA LA INDUSTRIA 4.0/es
dc.relation.projectIDinfo:eu-repo/grantAgreement/MIU//PRX21%2F00638/ES/es
dc.journal.titleEcological Informaticses
dc.volume.number74es
dc.page.initial101994es
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones


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