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@INPROCEEDINGS{Alia:910286,
      author       = {Alia, Ahmed and Maree, Mohammed and Chraibi, Mohcine},
      title        = {{A} {R}eal-{T}ime {N}eural {N}etwork-based {S}ystem for
                      {P}ushing {D}etection in {C}rowded {E}vent {E}ntrances},
      reportid     = {FZJ-2022-03728},
      year         = {2022},
      abstract     = {Pushing 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.},
      month         = {Oct},
      date          = {2022-10-15},
      organization  = {Traffic and Granular Flow, Indian
                       Institute of Technology Delhi (India),
                       15 Oct 2022 - 17 Oct 2022},
      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},
      url          = {https://juser.fz-juelich.de/record/910286},
}