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dc.contributor.authorAljaloud, Abdulaziz Salamah
dc.contributor.authorUllah, Habib
dc.date.accessioned2022-03-07T12:38:49Z
dc.date.available2022-03-07T12:38:49Z
dc.date.issued2021-05-17
dc.description.abstractAnalyzing unusual events is significantly important for video surveillance to ensure people safety. These events are characterized by irregular patterns that do not conform to the expected behavior in the surveillance scenes. We present a novel irregularity-aware semi-supervised deep learning model (IA-SSLM) for detection of unusual events. While most existing works depend on the availability of large amount of labeled data for training, our proposed method utilizes a semi-supervised deep model to automatically learn feature representations from limited number of labeled data samples. Our method extracts meaningful information from both labeled and unlabeled data during the training stage to improve the performance. For this purpose, we explore the concept of consistency regularization and entropy minimization to output confident predictions on unlabeled data. For experimental analysis, we consider various standard and diverse datasets. The results show that our IA-SSLM method outperforms several reference methods using different performance metrics.en_US
dc.identifier.citationAljaloud, Ullah. IA-SSLM: Irregularity-Aware Semi-Supervised Deep Learning Model for Analyzing Unusual Events in Crowds. IEEE Access. 2021;9:73327-73334en_US
dc.identifier.cristinIDFRIDAID 2005588
dc.identifier.doi10.1109/ACCESS.2021.3081050
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/10037/24302
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.journalIEEE Access
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.titleIA-SSLM: Irregularity-Aware Semi-Supervised Deep Learning Model for Analyzing Unusual Events in Crowdsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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