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001028429 1001_ $$0P:(DE-Juel1)185971$$aAlia, Ahmed$$b0$$eCorresponding author$$ufzj
001028429 245__ $$aArtificial Intelligence Framework for Video Analytics: Detecting Pushing in Crowds$$f - 2024
001028429 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2024
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001028429 4900_ $$aSchriften des Forschungszentrums Jülich IAS Series$$v61
001028429 502__ $$aDissertation, Univ. Wuppertal, 2024$$bDissertation$$cUniv. Wuppertal$$d2024
001028429 520__ $$aIn the modern era, data has become more complex, posing additional challenges to conventional data analysis methods. This is where Artificial Intelligence comes into play, specifically Deep Learning algorithms. These algorithms can analyze such data automatically, quickly, and accurately. Moreover, they can explore complex relationships between variables and identify non-linear patterns humans may not perceive. Leveraging this potential, Deep Learning has recently become pivotal in analyzing complex data, such as video data, arising from human crowds to enhance safety. Despite considerable advancements, some challenging problems in crowd dynamics still need to be solved efficiently and automatically.
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