% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Alia:1003812,
      author       = {Alia, Ahmed and Maree, Mohammed and Chraibi, Mohcine},
      title        = {{DL}4{P}u{D}e: {D}eep-{L}earning {F}ramework for {P}ushing
                      {D}etection in {P}edestrian {D}ynamics},
      reportid     = {FZJ-2023-01262},
      year         = {2023},
      abstract     = {At crowded event entrances, some pedestrians start pushing
                      others to gain faster access to the events, resulting in
                      dangerous situations. Pushing identification in video
                      recordings of events is crucial for understanding pushing
                      dynamics, thereby managing entrances safely. This talk
                      presents a deep-learning framework to help researchers
                      automatically identify pushing in videos of crowds. The
                      framework consists of four modules: (1) Optical Flow
                      Estimator that uses a pre-trained optical flow model to
                      estimate the dense displacement fields from input video. (2)
                      Wheel Visualization for generating motion information maps
                      from the displacement fields. (3) EfficientNet-B0 Classifier
                      that aims to identify pushing behavior from the maps. (4) A
                      False Reduction and Annotation module; to reduce the number
                      of false identifications of the classifier, annotate the
                      regions of pushing and output the annotated video. We used
                      five real-world ground truth of pushing behavior videos for
                      the evaluation. Experimental results show that the framework
                      achieves $86\%$ accuracy. The framework is open-source and
                      available at https://github.com/PedestrianDynamics/DL4PuDe.},
      month         = {Feb},
      date          = {2023-02-20},
      organization  = {Conference for Research Software
                       Engineering in Germany, Paderborn
                       (Germany), 20 Feb 2023 - 22 Feb 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},
      url          = {https://juser.fz-juelich.de/record/1003812},
}