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dc.contributor.authorBenito Picazo, Jesús
dc.contributor.authorDomínguez-Merino, Enrique 
dc.contributor.authorPalomo, Esteban J.
dc.contributor.authorLópez-Rubio, Ezequiel 
dc.contributor.authorOrtiz-de-lazcano-Lobato, Juan Miguel 
dc.date.accessioned2018-07-20T10:30:55Z
dc.date.available2018-07-20T10:30:55Z
dc.date.issued2018
dc.identifier.urihttps://hdl.handle.net/10630/16324
dc.description.abstractAutomatic video surveillance systems are usually designed to detect anomalous objects being present in a scene or behaving dangerously. In order to perform adequately, they must incorporate models able to achieve accurate pattern recognition in an image, and deep learning neural networks excel at this task. However, exhaustive scan of the full image results in multiple image blocks or windows to analyze, which could make the time performance of the system very poor when implemented on low cost devices. This paper presents a system which attempts to detect abnormal moving objects within an area covered by a PTZ camera while it is panning. The decision about the block of the image to analyze is based on a mixture distribution composed of two components: a uniform probability distribution, which represents a blind random selection, and a mixture of Gaussian probability distributions. Gaussian distributions represent windows in the image where anomalous objects were detected previously and contribute to generate the next window to analyze close to those windows of interest. The system is implemented on a Raspberry Pi microcontroller-based board, which enables the design and implementation of a low-cost monitoring system that is able to perform image processing.en_US
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.en_US
dc.language.isoengen_US
dc.publisherIEEE
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRedes neuronales (Informática)en_US
dc.subject.otherForeground detectionen_US
dc.subject.otherFeed forward neural networken_US
dc.subject.otherPTZ cameraen_US
dc.subject.otherConvolutional neural networken_US
dc.titleDeep learning-based anomalous object detection system powered by microcontroller for PTZ camerasen_US
dc.typeinfo:eu-repo/semantics/workingPaperen_US
dc.centroE.T.S.I. Informáticaen_US
dc.relation.eventtitle2018 IEEE World Congress on Computational Intelligenceen_US
dc.relation.eventplaceRío de Janeiro, Brasilen_US
dc.relation.eventdateJulio de 2018en_US


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