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dc.contributor.authorGarcía-González, Jorge
dc.contributor.authorMolina-Cabello, Miguel Ángel 
dc.contributor.authorLuque-Baena, Rafael Marcos 
dc.contributor.authorOrtiz-de-Lazcano-Lobato, Juan Miguel 
dc.contributor.authorLópez-Rubio, Ezequiel 
dc.date.accessioned2022-04-21T06:47:35Z
dc.date.available2022-04-21T06:47:35Z
dc.date.issued2021-12
dc.identifier.citationGarcía-González, Jorge ; Molina Cabello, Miguel Ángel ; Luque-Baena, Rafael ; Ortiz-de-lazcano-Lobato, Juan Miguel ; López-Rubio, Ezequiel. Road pollution estimation from vehicle tracking in surveillance videos by deep convolutional neural networks.Applied Soft Computing Volume 113, Part B, December 2021, 107950. https://doi.org/10.1016/j.asoc.2021.107950es_ES
dc.identifier.urihttps://hdl.handle.net/10630/23957
dc.description.abstractAir quality and reduction of emissions in the transport sector are determinant factors in achieving a sustainable global climate. The monitoring of emissions in traffic routes can help to improve route planning and to design strategies that may make the pollution levels to be reduced. In this work, a method which detects the pollution levels of transport vehicles from the images of IP cameras by means of computer vision techniques and neural networks is proposed. Specifically, for each sequence of images, a homography is calculated to correct the camera perspective and determine the real distance for each pixel. Subsequently, the trajectory of each vehicle is computed by applying convolutional neural networks for object detection and tracking algorithms. Finally, the speed in each frame and the pollution emitted by each vehicle are determined. Experimental results on several datasets available in the literature support the feasibility and scalability of the system as an emission control strategy.es_ES
dc.description.sponsorshipThis work is partially supported by the Ministry of Science, Innovation and Universities of Spain under grant RTI2018-094645-B-I00, roject name ‘‘Automated detection with low-cost hardware of unusual activities n video sequences’’. It is also partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name ‘‘Detection of anomalous behavior agents by deep learning in low-cost video surveillance intelligent systems’’. All of them include funds from the European Regional Development Fund (ERDF). It is also partially supported by the University of Malaga (Spain) under grants B1-2019_01, project name ‘‘Anomaly detection on roads by moving cameras’’, and B1-2019_02, project name ‘‘Self-Organizing Neural Systems for Non-Stationary Environments’’. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga.thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs. Finally, the authors thankfully acknowledge the grant of the Universidad de Málaga and the Instituto de Investigación Biomédica de Málaga - IBIMA. Funding for Open Access charge: University of Málaga/CBUA
dc.language.isoenges_ES
dc.publisherELSEVIERes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAire -- Contaminaciónes_ES
dc.subject.otherTraffic air pollutiones_ES
dc.subject.otherObject detectiones_ES
dc.subject.otherDeep learninges_ES
dc.subject.otherVideo surveillancees_ES
dc.titleRoad pollution estimation from vehicle tracking in surveillance videos by deep convolutional neural networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.identifier.doihttps://doi.org/10.1016/j.asoc.2021.107950
dc.rights.ccAtribución 4.0 Internacional*


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