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dc.contributor.authorQasim Gandapur, Maryam
dc.contributor.authorVerdú, Elena
dc.date2023
dc.date.accessioned2023-10-31T11:13:20Z
dc.date.available2023-10-31T11:13:20Z
dc.identifier.citationQasim, M., & Verdu, E. (2023). Video anomaly detection system using deep convolutional and recurrent models. Results in Engineering, 18, 101026.es_ES
dc.identifier.issn2590-1230
dc.identifier.urihttps://reunir.unir.net/handle/123456789/15510
dc.description.abstractAutomatic identification of anomalies in video surveillance is an interesting research field. Even though interactive multimedia anomaly detection algorithms have been developed, it is still hard for video surveillance to find unusual things like illegal activities and crimes. In this study, a deep convolutional neural network (CNN) and a simple recurrent unit (SRU) are used to build an automated system that can find anomalies in videos. The ResNet architecture takes high-level feature representations from the video frames that come in, while the SRU collects temporal features. The SRU has expressive recurrence and allows for highly parallelized implementation, which makes the video anomaly detection system more accurate. In the study, three models to detect anomalies are suggested as ResNet18 + SRU, ResNet34 + SRU, and ResNet50 + SRU, respectively. The suggested models are examined using the UCF-Crime dataset. This study made a clear distinction between normal and unusual actions, showing that CNN + SRU were able to put each unusual action in the right category. Using the UCF-Crime dataset, ResNet18 + SRU achieved 88.92% accuracy, ResNet34 + SRU achieved 89.34% accuracy, and ResNet50 + SRU achieved 91.24% accuracy. Furthermore, the proposed models demonstrated significantly higher performance accuracy and outscored the comparable deep learning models.es_ES
dc.language.isoenges_ES
dc.publisherResults in Engineeringes_ES
dc.relation.ispartofseries;vol. 18
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S2590123023001536?via%3Dihubes_ES
dc.rightsopenAccesses_ES
dc.subjectanomaly detectiones_ES
dc.subjectCNNes_ES
dc.subjectdeep learninges_ES
dc.subjectResNetes_ES
dc.subjectsimple recurrent unit (SRU)es_ES
dc.subjectUCF-Crimees_ES
dc.subjectvideo surveillancees_ES
dc.subjectScopuses_ES
dc.subjectEmerginges_ES
dc.titleVideo anomaly detection system using deep convolutional and recurrent modelses_ES
dc.typeArticulo Revista Indexadaes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.1016/j.rineng.2023.101026


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