000910286 001__ 910286
000910286 005__ 20221021130357.0
000910286 0247_ $$2Handle$$a2128/32081
000910286 037__ $$aFZJ-2022-03728
000910286 041__ $$aEnglish
000910286 1001_ $$0P:(DE-Juel1)185971$$aAlia, Ahmed$$b0$$eCorresponding author
000910286 1112_ $$aTraffic and Granular Flow$$cIndian Institute of Technology Delhi$$d2022-10-15 - 2022-10-17$$gTGF$$wIndia
000910286 245__ $$aA Real-Time Neural Network-based System for Pushing Detection in Crowded Event Entrances
000910286 260__ $$c2022
000910286 3367_ $$033$$2EndNote$$aConference Paper
000910286 3367_ $$2DataCite$$aOther
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000910286 520__ $$aPushing is a behavior that is often used by some pedestrians, especially in crowded event entrances, to gain faster access to events. Such behavior increases the crowd's density, affecting crowd comfort and safety. Real-time detection of pushing behavior is crucial for event organizers; to react to pushing behavior at an early stage, hence avoiding uncomfortable and unsafe situations in the crowd. Recently, some approaches have been proposed to automatically identify pushing behavior from videos of crowded event entrances. However, these approaches could not detect the behavior in real-time or near real-time. Accordingly, in this research, we present a real-time system for automatically detecting and localizing pushing behavior in video frames. As shown in the figure below, the proposed system includes three main modules: 1) Target frames retrieving and processing, which first aims to select the required frames and then extract the entrance area from them. 2) Spatial motion extraction uses deep optical flow based on GPU to estimate the spatial visual motion with speed and direction information from the entrance area. 3) Pushing detection is based mainly on a supervised CNN-based classifier and the extracted motion information; to identify and annotate pushing behavior. We build a dataset from several real-world experiments (videos with ground truth) to evaluate the proposed system. Experimental results show that our system achieves promising performance in terms of accuracy and computational time.
000910286 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
000910286 536__ $$0G:(BMBF)01DH16027$$aPilotprojekt zur Entwicklung eines palästinensisch-deutschen Forschungs- und Promotionsprogramms 'Palestinian-German Science Bridge' (01DH16027)$$c01DH16027$$x1
000910286 7001_ $$0P:(DE-HGF)0$$aMaree, Mohammed$$b1
000910286 7001_ $$0P:(DE-Juel1)132077$$aChraibi, Mohcine$$b2
000910286 8564_ $$uhttps://tgf.iitd.ac.in/Abstracts.pdf
000910286 8564_ $$uhttps://juser.fz-juelich.de/record/910286/files/abstract.pdf$$yOpenAccess
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000910286 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132077$$aForschungszentrum Jülich$$b2$$kFZJ
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000910286 9141_ $$y2022
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