Detection of Violent Activities Using Deep Learning Algorithms

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dc.contributor.advisorDr. Atiqur Rahman
dc.contributor.authorTasmiah Sarker
dc.contributor.authorFayeeka Simran
dc.contributor.authorZahiduzzaman Anik
dc.contributor.id1912844642
dc.contributor.id1911656642
dc.contributor.id1632091042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025
dc.date.accessioned2025-07-21T05:13:16Z
dc.date.available2025-07-21T05:13:16Z
dc.date.issued2022
dc.description.abstractThe creation of a method for violence detection in surveillance footage using automatic analysis is crucial. In this study, we propose a deep neural network to recognize violent videos. A convolutional neural network and an ImageNet model that has already been trained are used to extract frame level characteristics from a movie. Then, using a long short-term memory variation that makes use of fully connected layers and leaky rectified linear units, the frame level features are aggregated. Convolutional neural networks are capable of recording localized spatio-temporal information that allow the analysis of local motion in the video, in addition to long short-term memory. On three common benchmark datasets, the accuracy of recognition is used to further assess the performance. We also contrasted the findings of our system with those from other methodologies to ascertain the capabilities of our proposed model. The suggested solution outperforms cutting-edge techniques while processing the videos in real-time.
dc.description.degreeUndergraduate
dc.identifier.cd600000195
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1296
dc.language.isoen
dc.publisherNorth South University
dc.rights©NSU Library
dc.titleDetection of Violent Activities Using Deep Learning Algorithms
oaire.citation.endPage70
oaire.citation.startPage1
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