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000916435 037__ $$aFZJ-2022-06229
000916435 041__ $$aEnglish
000916435 1001_ $$0P:(DE-Juel1)185971$$aAlia, Ahmed$$b0$$eCorresponding author$$ufzj
000916435 1112_ $$a2022 IEEE/ACS 19th International Conference on Computer Systems and Applications$$cZayed University, Abu Dhabi$$d2022-12-05 - 2022-12-07$$gAICCSA$$wU Arab Emirates
000916435 245__ $$aA Fast Hybrid Deep Neural Network Model for pushing behavior detection in human crowds
000916435 260__ $$bIEEE$$c2023
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000916435 520__ $$aDeep learning technology is regarded as one of the latest advances in data science and analytics due to its learning abilities from the data. As a result, deep learning is widely applied in the human crowd analysis domain. Although it has achieved remarkable success in this area, a fast and robust model for pushing behavior detection in the human crowd is unavailable. This paper proposes a model that allows crowd-monitoring systems to detect pushing behavior early, helping organizers make timely decisions before dangerous situations appear. This particularly becomes more challenging when applied to real-time video streams of crowded events, which the proposed model accomplishes with reasonable time latency. To achieve this, the model employs a hybrid deep neural network.
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000916435 536__ $$0G:(BMBF)01DH16027$$aPilotprojekt zur Entwicklung eines palästinensisch-deutschen Forschungs- und Promotionsprogramms 'Palestinian-German Science Bridge' (01DH16027)$$c01DH16027$$x1
000916435 588__ $$aDataset connected to CrossRef Conference
000916435 7001_ $$0P:(DE-HGF)0$$aMaree, Mohammed$$b1
000916435 7001_ $$0P:(DE-Juel1)132077$$aChraibi, Mohcine$$b2$$ufzj
000916435 773__ $$a10.1109/AICCSA56895.2022.10017883
000916435 8564_ $$uhttps://ieeexplore.ieee.org/document/10017883
000916435 8564_ $$uhttps://juser.fz-juelich.de/record/916435/files/Ahmed%20Alia%20Extended%20Abstract-AICCSA2022.pdf$$yOpenAccess
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000916435 9141_ $$y2023
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