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001034451 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-07217
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001034451 1001_ $$0P:(DE-HGF)0$$aAbubaker, Mohammed$$b0
001034451 1112_ $$aTraffic and Granular Flow$$cLyon$$d2024-12-02 - 2024-12-05$$gTGF24$$wFrance
001034451 245__ $$aA Novel Dataset for Detecting Pedestrian Heads in Crowds Using Deep Learning Algorithms
001034451 260__ $$c2024
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001034451 520__ $$aThe automatic detection of pedestrian heads in crowded environments is crucial for various crowd analysis and management tasks, including crowd counting, density estimation, pedestrian trajectory extraction, and behavior detection. Despite advancements in deep learning algorithms for object detection, existing studies struggle with pedestrian head detection in crowded situations such as railway platforms and event entrances, where risks frequently arise. One main reason for the poor head detection performance is the underrepresentation of such scenarios in existing datasets. These scenarios are particularly challenging due to variations in lighting conditions, viewpoints, occlusions, scale changes, indoor/outdoor environments,and weather conditions.To narrow this gap, we introduce a novel, diverse, and high-resolution dataset of human heads in crowds at Railway Platforms and Event Entrances, named the RPEE-Heads dataset.
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001034451 536__ $$0G:(BMBF)01DH16027$$aPilotprojekt zur Entwicklung eines palästinensisch-deutschen Forschungs- und Promotionsprogramms 'Palestinian-German Science Bridge' (01DH16027)$$c01DH16027$$x1
001034451 7001_ $$0P:(DE-HGF)0$$aAlsadder, Zubayda$$b1
001034451 7001_ $$0P:(DE-HGF)0$$aAbdelhaq, Hamed$$b2
001034451 7001_ $$0P:(DE-Juel1)132077$$aChraibi, Mohcine$$b3
001034451 7001_ $$0P:(DE-Juel1)132064$$aBoltes, Maik$$b4
001034451 7001_ $$0P:(DE-Juel1)185971$$aAlia, Ahmed$$b5$$eCorresponding author
001034451 8564_ $$uhttps://juser.fz-juelich.de/record/1034451/files/A%20Novel%20Dataset%20for%20Detecting%20Pedestrian%20Heads%20in%20Crowds.pdf$$yOpenAccess
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001034451 9141_ $$y2024
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