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dc.contributor.authorBartolomé Hornillos, César
dc.contributor.authorSan José Revuelta, Luis Miguel 
dc.contributor.authorAguiar Pérez, Javier Manuel 
dc.contributor.authorGarcía Serrada, Carlos
dc.contributor.authorVara Pazos, Eduardo
dc.contributor.authorCasaseca de la Higuera, Juan Pablo 
dc.date.accessioned2024-06-13T07:53:32Z
dc.date.available2024-06-13T07:53:32Z
dc.date.issued2024
dc.identifier.citationSensors, 2024, Vol. 24, Nº. 6, 1822es
dc.identifier.issn1424-8220es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/68101
dc.descriptionProducción Científicaes
dc.description.abstractWe present an automatic road incident detector characterised by a low computational complexity for easy implementation in affordable devices, automatic adaptability to changes in scenery and road conditions, and automatic detection of the most common incidents (vehicles with abnormal speed, pedestrians or objects falling on the road, vehicles stopped on the shoulder, and detection of kamikaze vehicles). To achieve these goals, different tasks have been addressed: lane segmentation, identification of traffic directions, and elimination of unnecessary objects in the foreground. The proposed system has been tested on a collection of videos recorded in real scenarios with real traffic, including areas with different lighting. Self-adaptability (plug and play) to different scenarios has been tested using videos with significant scene changes. The achieved system can process a minimum of 80 video frames within the camera’s field of view, covering a distance of 400 m, all within a span of 12 s. This capability ensures that vehicles travelling at speeds of 120 km/h are seamlessly detected with more than enough margin. Additionally, our analysis has revealed a substantial improvement in incident detection with respect to previous approaches. Specifically, an increase in accuracy of 2–5% in automatic mode and 2–7% in semi-automatic mode. The proposed classifier module only needs 2.3 MBytes of GPU to carry out the inference, thus allowing implementation in low-cost devices.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherMDPIes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectSecurityes
dc.subjectSystems and Data Securityes
dc.subjectArtificial intelligencees
dc.subjectTraffic safetyes
dc.subjectSeguridad en carreteraes
dc.subjectAutomobiles - Safety applianceses
dc.subjectSeguridad viales
dc.subjectTráfico, Ingeniería del - Informáticaes
dc.subjectHighway communicationses
dc.subjectMachine learninges
dc.subjectAprendizaje automáticoes
dc.subjectSelf-adaptive softwarees
dc.subjectVideo surveillancees
dc.subjectVideoes
dc.subjectArtificial intelligencees
dc.titleA self-adaptive automatic incident detection system for road surveillance based on deep learninges
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2024 The authorses
dc.identifier.doi10.3390/s24061822es
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/24/6/1822es
dc.identifier.publicationfirstpage1822es
dc.identifier.publicationissue6es
dc.identifier.publicationtitleSensorses
dc.identifier.publicationvolume24es
dc.peerreviewedSIes
dc.description.projectJunta de Castilla y León, Instituto de Competitividad Empresarial y Fondo Europeo de Desarrollo Regional (FEDER) - (grants 04/18/VA/0008, 04/18/VA/0013, 04/20/VA/0130)es
dc.description.projectEuropean Union’s Horizon 2020 and Innovation Program Marie Skłodowska-Curie - (Grant 101008297)es
dc.identifier.essn1424-8220es
dc.rightsAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones
dc.subject.unesco1203.04 Inteligencia Artificiales
dc.subject.unesco1203.25 Diseño de Sistemas Sensoreses


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