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@INPROCEEDINGS{Alia:1008638,
      author       = {Alia, Ahmed and Maree, Mohammed and Chraibi, Mohcine},
      title        = {{A} {N}ovel {V}oronoi-based {C}onvolutional {N}eural
                      {N}etwork {A}pproach for {C}rowd {V}ideo {A}nalysis and
                      {P}ushing {P}erson {D}etection},
      reportid     = {FZJ-2023-02439},
      year         = {2023},
      abstract     = {At crowded event entrances, some individuals attempt to
                      push others to move quickly and enter the event faster. Such
                      behavior can increase the density over time, which could not
                      only threaten the comfort of pedestrians but also cause
                      life-threatening situations. To prevent such incidents,
                      event organizers and security personnel need to understand
                      the pushing dynamics in crowds. One effective way to achieve
                      this is by detecting pushing individuals from video
                      recordings of crowds. Recently, some automatic approaches
                      have been developed to help researchers identify pushing
                      behavior in crowd videos. However, these approaches only
                      detect the regions where pushing occurs rather than the
                      pushing individuals, limiting their contribution to
                      understanding pushing dynamics in crowds. To overcome the
                      limitations of previous methods, this work presents a novel
                      Voronoi-based Convolutional Neural Network (CNN) approach
                      for pushing person detection in crowd videos. As depicted in
                      Figure 1, the proposed approach comprises two main phases:
                      feature extraction and labeling. In the first phase, a new
                      Voronoi-based method is developed and utilized to identify
                      the local regions of individuals, employing both the video
                      and the associated trajectory data as inputs. It then uses
                      EfficientNetB0 CNN to extract the deep features of
                      individual behavior from the identified regions. In
                      contrast, the labeling phase utilizes a fully connected
                      layer with a Sigmoid activation function to analyze the
                      extracted deep features and identify the pushing persons.
                      Finally, this phase annotates the pushing persons in the
                      video. Furthermore, this work produces a novel dataset using
                      five real-world experiments with their associated ground
                      truths, which is utilized for training and evaluating the
                      proposed approach. The resulting dataset consists of 11717
                      local regions, of which 3067 represent pushing samples, and
                      8650 represent nonpushing samples. The experimental outcomes
                      demonstrate that the proposed approach attained an accuracy
                      of $83\%$ and a f1-score of $80\%.$},
      month         = {Jun},
      date          = {2023-06-12},
      organization  = {Helmholtz AI Conference 2023, Hamburg
                       (Germany), 12 Jun 2023 - 14 Jun 2023},
      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-2023-02439},
      url          = {https://juser.fz-juelich.de/record/1008638},
}