A Temporal fusion approach for video classifcation with convolutional and LSTM neural networks applied to violence detection

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<dc:creator>Oliveira Lima, Jean Phelipe de </dc:creator>
<dc:creator>Seródio Figueiredo, Carlos Maurício </dc:creator>
<dc:date>2021-02-15</dc:date>
<dc:description xml:lang="es">Sumario: In modern smart cities, there is a quest for the highest level of integration and automation service. In the surveillance sector, one of the main challenges is to automate the analysis of videos in real-time to identify critical situations. This paper presents intelligent models based on Convolutional Neural Networks (in which the MobileNet, InceptionV3 and VGG16 networks had used), LSTM networks and feedforward networks for the task of classifying videos under the classes "Violence" and "Non-Violence", using for this the RLVS database. Di rent data representations held used according to the Temporal Fusion techniques. The best outcome achieved was 0.91 and 0.90 of Accuracy and F1-Score, respectively, a higher result compared to those found in similar researches for works conducted on the same database. </dc:description>
<dc:format xml:lang="en">application/pdf</dc:format>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/record.do?id=175312</dc:identifier>
<dc:language>spa</dc:language>
<dc:rights xml:lang="es">La copia digital se distribuye bajo licencia "Attribution 4.0 International (CC BY NC 4.0)" - https://creativecommons.org/licenses/by-nc/4.0</dc:rights>
<dc:subject xml:lang="es">Inteligencia artificial</dc:subject>
<dc:subject xml:lang="es">Automatización</dc:subject>
<dc:subject xml:lang="es">Violencia</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">A Temporal fusion approach for video classifcation with convolutional and LSTM neural networks applied to violence detection</dc:title>
<dc:relation xml:lang="es">En: Revista Iberoamericana de Inteligencia Artificial. - IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018- = ISSN 1988-3064. - 15/02/2021 Volumen 24 Número 67 - febrero 2021 , p. 40-50</dc:relation>
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