001034454 001__ 1034454
001034454 005__ 20241223113912.0
001034454 0247_ $$2doi$$a10.48550/arXiv.2411.18164
001034454 037__ $$aFZJ-2024-07220
001034454 041__ $$aEnglish
001034454 088__ $$2arXiv$$ahttps://doi.org/10.48550/arXiv.2411.18164
001034454 1001_ $$0P:(DE-HGF)0$$aAbubaker, Mohammed$$b0
001034454 245__ $$aRPEE-Heads: A Novel Benchmark For Pedestrian Head Detection in Crowd Videos
001034454 260__ $$barXiv$$c2024
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001034454 3367_ $$2ORCID$$aWORKING_PAPER
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001034454 3367_ $$2BibTeX$$aARTICLE
001034454 3367_ $$2DataCite$$aOutput Types/Working Paper
001034454 520__ $$aThe automatic detection of pedestrian heads in crowded environments is essential for crowd analysis and management tasks, particularly in high-risk settings such as railway platforms and event entrances. These environments, characterized by dense crowds and dynamic movements, are underrepresented in public datasets, posing challenges for existing deep learning models. To address this gap, we introduce the Railway Platforms and Event Entrances-Heads (RPEE-Heads) dataset, a novel, diverse, highresolution, and accurately annotated resource. It includes 109,913 annotated pedestrian heads across 1,886 images from 66 video recordings, with an average of 56.2 heads per image. Annotations include bounding boxes for visible head regions. In addition to introducing the RPEE-Heads dataset, this paper evaluates eight state-of-the-art object detection algorithms using the RPEE-Heads dataset and analyzes the impact of head size on detection accuracy. The experimental results show that You Only Look Once v9 and Real-Time Detection Transformer outperform the other algorithms, achieving mean average precisions of 90.7% and 90.8%, with inference times of 11 and 14 milliseconds, respectively. Moreover, the findings underscore the need for specialized datasets like RPEE-Heads for training and evaluating accurate models for head detection in railway platforms and event entrances. The dataset and pretrained models are available at https://doi.org/10.34735/ped.2024.2.
001034454 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001034454 536__ $$0G:(BMBF)01DH16027$$aPilotprojekt zur Entwicklung eines palästinensisch-deutschen Forschungs- und Promotionsprogramms 'Palestinian-German Science Bridge' (01DH16027)$$c01DH16027$$x1
001034454 588__ $$aDataset connected to DataCite
001034454 650_7 $$2Other$$aComputer Vision and Pattern Recognition (cs.CV)
001034454 650_7 $$2Other$$aMachine Learning (cs.LG)
001034454 650_7 $$2Other$$aFOS: Computer and information sciences
001034454 7001_ $$0P:(DE-HGF)0$$aAlsadder, Zubayda$$b1
001034454 7001_ $$0P:(DE-HGF)0$$aAbdelhaq, Hamed$$b2$$eCorresponding author
001034454 7001_ $$0P:(DE-Juel1)132064$$aBoltes, Maik$$b3$$eCorresponding author$$ufzj
001034454 7001_ $$0P:(DE-Juel1)185971$$aAlia, Ahmed$$b4$$ufzj
001034454 773__ $$a10.48550/arXiv.2411.18164
001034454 8564_ $$uhttps://arxiv.org/abs/2411.18164
001034454 909CO $$ooai:juser.fz-juelich.de:1034454$$pVDB
001034454 9141_ $$y2024
001034454 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132064$$aForschungszentrum Jülich$$b3$$kFZJ
001034454 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)185971$$aForschungszentrum Jülich$$b4$$kFZJ
001034454 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001034454 920__ $$lyes
001034454 9201_ $$0I:(DE-Juel1)IAS-7-20180321$$kIAS-7$$lZivile Sicherheitsforschung$$x0
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