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Low-Cost CNN for Automatic Violence Recognition on Embedded System

2022Journal article Open access
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dc.contributor.authorVieira, J. C.
dc.contributor.authorSartori, Andreza
dc.contributor.authorStefenon, Stéfano Frizzo
dc.contributor.authorPerez, Fabio Luis
dc.contributor.authorSchneider De Jesus, Gabriel
dc.contributor.authorLEITHARDT, VALDERI
dc.date.accessioned2023-02-01T18:43:24ZPT
dc.date.available2023-02-01T18:43:24ZPT
dc.date.issued2022PT
dc.date.updated2022-03-12T11:13:49Z
dc.description.abstractDue to the increasing number of violence cases, there is a high demand for efficient monitoring systems, however, these systems can be susceptible to failure. Therefore, this work proposes the analysis and application of low-cost Convolutional Neural Networks (CNNs) techniques to automatically recognize and classify suspicious events. Thus, it is possible to alert and assist the monitoring process with a reduced deployment cost. For this purpose, a dataset with violence and non-violence actions in scenes of crowded and non-crowded environments was assembled. The mobile CNNs architectures were adapted and obtained a classification accuracy of up to 92.05%, with a low number of parameters. To demonstrate the models validity, a prototype was developed by using an embedded Raspberry Pi platform, able to execute a model in real-time with 4 frames-per-second of speed. In addition, a warning system was developed to recognize pre-fight behavior and anticipate violent acts, alerting security to potential situations.pt_PT
dc.description.versionN/Apt_PT
dc.identifier.doi10.1109/ACCESS.2022.3155123pt_PT
dc.identifier.slugcv-prod-2950384
dc.identifier.urihttp://hdl.handle.net/10400.26/43566PT
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationResearch Center for Endogenous Resource Valorization
dc.relationCOPELABS - Cognitive and People-centric Computing R&D Unit
dc.subjectNeural networks,pt_PT
dc.subjectartificial neural networks,pt_PT
dc.subjectimage processing,pt_PT
dc.subjectimage classificationpt_PT
dc.titleLow-Cost CNN for Automatic Violence Recognition on Embedded Systempt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Center for Endogenous Resource Valorization
oaire.awardTitleCOPELABS - Cognitive and People-centric Computing R&D Unit
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05064%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04111%2F2020/PT
oaire.citation.endPage1pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleIEEE Accesspt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameVieira
person.familyNameSartori
person.familyNameStefenon
person.familyNamePerez
person.familyNameSchneider de Jesus
person.familyNameREIS QUIETINHO LEITHARDT
person.givenNameJoelton Cezar
person.givenNameAndreza
person.givenNameStefano Frizzo
person.givenNameFabio Luis
person.givenNameGabriel
person.givenNameVALDERI
person.identifier916543
person.identifierJsOq45sAAAAJ&hl=pt-PT
person.identifier.ciencia-id4019-BB36-7F74
person.identifier.ciencia-id0614-5834-E7F3
person.identifier.orcid0000-0002-4677-7160
person.identifier.orcid0000-0002-3982-8767
person.identifier.orcid0000-0002-3723-616X
person.identifier.orcid0000-0001-5223-1562
person.identifier.orcid0000-0001-9357-1248
person.identifier.orcid0000-0003-0446-9271
person.identifier.ridAAD-7639-2019
person.identifier.scopus-author-id57194147390
person.identifier.scopus-author-id55813849500
person.identifier.scopus-author-id35303109600
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.cv.cienciaid0614-5834-E7F3 | Valderi Reis Quietinho Leithardt
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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