dc.creator | Ruiz Santaquiteria Alegre, Jesús | es |
dc.creator | Velasco Mata, Alberto | es |
dc.creator | Vallez Enano, Noelia | es |
dc.creator | Bueno, Gloria | es |
dc.creator | Álvarez García, Juan Antonio | es |
dc.creator | Deniz, Óscar | es |
dc.date.accessioned | 2021-09-21T08:42:26Z | |
dc.date.available | 2021-09-21T08:42:26Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Ruiz 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.uri | https://hdl.handle.net/11441/126063 | |
dc.description.abstract | Closed-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.sponsorship | Ministerio de Economía y Empresa TIN2017-82113-C2-2-R | es |
dc.description.sponsorship | Junta de Castilla.La Mancha SB-PLY/17/180501/000543 | es |
dc.format | application/pdf | es |
dc.format.extent | 11 | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | IEEE Access, 9 (Jul 2021), 123815-123826. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | CCTV surveillance | es |
dc.subject | deep learning | es |
dc.subject | handgun detection | es |
dc.subject | human pose estimation | es |
dc.title | Handgun detection using combined human pose and weapon appearance | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistema Informáticos | es |
dc.relation.projectID | TIN2017-82113-C2-2-R | es |
dc.relation.projectID | SB-PLY/17/180501/000543 | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9529187 | es |
dc.identifier.doi | 10.1109/ACCESS.2021.3110335 | es |
dc.contributor.group | Universidad de Sevilla. TIC134: Sisitemas Informáticos | es |
dc.journaltitle | IEEE Access | es |
dc.publication.volumen | 9 | es |
dc.publication.issue | Jul 2021 | es |
dc.publication.initialPage | 123815 | es |
dc.publication.endPage | 123826 | es |
dc.contributor.funder | Ministerio de Economía y Empresa (MINECO). España | es |
dc.contributor.funder | Junta de Castilla-La Mancha | es |