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@INPROCEEDINGS{Abubaker:1034451,
      author       = {Abubaker, Mohammed and Alsadder, Zubayda and Abdelhaq,
                      Hamed and Chraibi, Mohcine and Boltes, Maik and Alia, Ahmed},
      title        = {{A} {N}ovel {D}ataset for {D}etecting {P}edestrian {H}eads
                      in {C}rowds {U}sing {D}eep {L}earning {A}lgorithms},
      reportid     = {FZJ-2024-07217},
      year         = {2024},
      abstract     = {The 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.},
      month         = {Dec},
      date          = {2024-12-02},
      organization  = {Traffic and Granular Flow, Lyon
                       (France), 2 Dec 2024 - 5 Dec 2024},
      subtyp        = {After Call},
      cin          = {IAS-7},
      cid          = {I:(DE-Juel1)IAS-7-20180321},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / Pilotprojekt zur
                      Entwicklung eines palästinensisch-deutschen Forschungs- und
                      Promotionsprogramms 'Palestinian-German Science Bridge'
                      (01DH16027)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(BMBF)01DH16027},
      typ          = {PUB:(DE-HGF)6},
      doi          = {10.34734/FZJ-2024-07217},
      url          = {https://juser.fz-juelich.de/record/1034451},
}