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Artículo
Real-time gun detection in CCTV: An open problem
dc.contributor.editor | Álvarez García, Juan Antonio | es |
dc.creator | Salazar González, Jose Luis | es |
dc.creator | Zaccaro, Carlos | es |
dc.creator | Álvarez García, Juan Antonio | es |
dc.creator | Soria Morillo, Luis Miguel | es |
dc.creator | Sancho Caparrini, Fernando | es |
dc.date.accessioned | 2020-09-24T08:29:52Z | |
dc.date.available | 2020-09-24T08:29:52Z | |
dc.date.issued | 2020-12 | |
dc.identifier.citation | Salazar González, J.L., Zaccaro, C., Álvarez García, J.A., Soria Morillo, L.M. y Sancho Caparrini, F. (2020). Real-time gun detection in CCTV: An open problem. Neural Networks, 132 (December 2020), 297-308. | |
dc.identifier.issn | 0893-6080 | es |
dc.identifier.uri | https://hdl.handle.net/11441/101429 | |
dc.description.abstract | Object detectors have improved in recent years, obtaining better results and faster inference time. However, small object detection is still a problem that has not yet a definitive solution. The autonomous weapons detection on Closed-circuit television (CCTV) has been studied recently, being extremely useful in the field of security, counter-terrorism, and risk mitigation. This article presents a new dataset obtained from a real CCTV installed in a university and the generation of synthetic images, to which Faster R-CNN was applied using Feature Pyramid Network with ResNet-50 resulting in a weapon detection model able to be used in quasi real-time CCTV (90 ms of inference time with an NVIDIA GeForce GTX-1080Ti card) improving the state of the art on weapon detection in a two stages training. In this work, an exhaustive experimental study of the detector with these datasets was performed, showing the impact of synthetic datasets on the training of weapons detection systems, as well as the main limitations that these systems present nowadays. The generated synthetic dataset and the real CCTV dataset are available to the whole research community. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2017-82113-C2-1-R | es |
dc.format | application/pdf | es |
dc.format.extent | 11 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Neural Networks, 132 (December 2020), 297-308. | |
dc.subject | Deep learning | es |
dc.subject | Convolutional neural network | es |
dc.subject | Weapon detection | es |
dc.subject | Feature Pyramid Network | es |
dc.subject | Synthetic data | es |
dc.subject | Data augmentation | es |
dc.title | Real-time gun detection in CCTV: An open problem | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/embargoedAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | TIN2017-82113-C2-1-R | es |
dc.date.embargoEndDate | 2022-12 | |
dc.relation.publisherversion | https://doi.org/10.1016/j.neunet.2020.09.013 | es |
dc.identifier.doi | 10.1016/j.neunet.2020.09.013 | es |
idus.validador.nota | https://www.sciencedirect.com/science/article/pii/S0893608020303361 | es |
dc.journaltitle | Neural Networks | es |
dc.publication.volumen | 132 | es |
dc.publication.issue | December 2020 | es |
dc.publication.initialPage | 297 | es |
dc.publication.endPage | 308 | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |
Ficheros | Tamaño | Formato | Ver | Descripción |
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