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dc.creatorRuiz Santaquiteria Alegre, Jesúses
dc.creatorVelasco Mata, Albertoes
dc.creatorVallez Enano, Noeliaes
dc.creatorBueno, Gloriaes
dc.creatorÁlvarez García, Juan Antonioes
dc.creatorDeniz, Óscares
dc.date.accessioned2021-09-21T08:42:26Z
dc.date.available2021-09-21T08:42:26Z
dc.date.issued2021
dc.identifier.citationRuiz Santaquiteria Alegre, J., Velasco Mata, A., Vallez Enano, N., Bueno, G., Álvarez García, J.A. y Deniz, Ó. (2021). Handgun detection using combined human pose and weapon appearance. IEEE Access, 9 (Jul 2021), 123815-123826.
dc.identifier.urihttps://hdl.handle.net/11441/126063
dc.description.abstractClosed-circuit television (CCTV) systems are essential nowadays to prevent security threats or dangerous situations, in which early detection is crucial. Novel deep learning-based methods have allowed to develop automatic weapon detectors with promising results. However, these approaches are mainly based on visual weapon appearance only. For handguns, body pose may be a useful cue, especially in cases where the gun is barely visible. In this work, a novel method is proposed to combine, in a single architecture, both weapon appearance and human pose information. First, pose keypoints are estimated to extract hand regions and generate binary pose images, which are the model inputs. Then, each input is processed in di erent subnetworks and combined to produce the handgun bounding box. Results obtained show that the combined model improves the handgun detection state of the art, achieving from 4.23 to 18.9 AP points more than the best previous approach.es
dc.description.sponsorshipMinisterio de Economía y Empresa TIN2017-82113-C2-2-Res
dc.description.sponsorshipJunta de Castilla.La Mancha SB-PLY/17/180501/000543es
dc.formatapplication/pdfes
dc.format.extent11es
dc.language.isoenges
dc.publisherIEEE Computer Societyes
dc.relation.ispartofIEEE Access, 9 (Jul 2021), 123815-123826.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectCCTV surveillancees
dc.subjectdeep learninges
dc.subjecthandgun detectiones
dc.subjecthuman pose estimationes
dc.titleHandgun detection using combined human pose and weapon appearancees
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistema Informáticoses
dc.relation.projectIDTIN2017-82113-C2-2-Res
dc.relation.projectIDSB-PLY/17/180501/000543es
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9529187es
dc.identifier.doi10.1109/ACCESS.2021.3110335es
dc.contributor.groupUniversidad de Sevilla. TIC134: Sisitemas Informáticoses
dc.journaltitleIEEE Accesses
dc.publication.volumen9es
dc.publication.issueJul 2021es
dc.publication.initialPage123815es
dc.publication.endPage123826es
dc.contributor.funderMinisterio de Economía y Empresa (MINECO). Españaes
dc.contributor.funderJunta de Castilla-La Manchaes

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Except where otherwise noted, this item's license is described as: Attribution-NonCommercial-NoDerivatives 4.0 Internacional