% 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:1019197,
      author       = {Alia, Ahmed and Maree, moahammed and Chraibi, Mohcine},
      title        = {{A}rtificial {I}ntelligence-based {E}arly {P}ushing
                      {D}etection in {L}ive {V}ideo {S}treams of {C}rowds},
      school       = {Wuppertal University},
      reportid     = {FZJ-2023-05241},
      year         = {2023},
      abstract     = {Entrances of crowded events are often set up as bottlenecks
                      for several reasons, such as access control, ticket
                      validation, or security check. In these scenarios, some
                      pedestrians could start pushing others or using gaps among
                      crowds to reduce their waiting time. Such behavior doesn’t
                      only limit the comfort zones but also leads to threatening
                      people’s safety. Early detection of pushing behavior may
                      assist security and organizers in making decisions on time,
                      enhancing the comfort and safety of the entrances.
                      Unfortunately, existing works reported in the literature to
                      detect pushing in crowds are limited and have not satisfied
                      the early detection requirements. For instance, Lügering et
                      al. [1] developed a manual rating system to understand when,
                      where and why pushing appears in video recordings of crowded
                      entrance areas. To overcome the limitations of manual
                      analysis, Alia et al. [2] proposed an automatic
                      deep-learning system for pushing detection. However, this
                      system does not meet the requirements of early detection. To
                      fulfill the early detection requirements, we present an
                      Artificial Intelligence framework for automatically
                      identifying pushing in the live camera stream in real-time.
                      Our framework consists of two main components: The first
                      component uses a pretrained deep optical flow model and
                      color wheel method to extract the movement of pixels from
                      the live stream of a crowd and represent this information
                      visually. The second component includes an adapted and
                      trained EfficientNetV2B0 model, which extracts deep features
                      from the motion information, and then identifies and
                      annotates pushing patches within the live stream. We created
                      a labeled dataset using five real-world experiments [3] with
                      their associated ground truths to train the adapted model
                      and evaluate the framework. The experimental setups mimic
                      the crowded event entrances, and two experts based on the
                      manual rating system [1] created the ground truths.
                      According to the experimental results, our framework
                      identified pushing patches with an accuracy of $87\%$ and
                      within a reasonable delay time. --- References: [1] Üsten,
                      E., Lügering, H. $\&$ Sieben, A., Pushing and Non-pushing
                      Forward Motion in Crowds: A Systematic Psychological
                      Observation Method for Rating Individual Behavior in
                      Pedestrian Dynamics, Collective Dynamics, 7 pp. 1-16, 2022.
                      --- [2] Alia, A., Maree, M. $\&$ Chraibi, M. A Hybrid Deep
                      Learning and Visualization Framework for Pushing Behavior
                      Detection in Pedestrian Dynamics, Sensors, 22, 4040, 2022.
                      ---[3] Pedestrian Dynamics Data Archive hosted by the
                      Forschungszentrum Jülich, P. Crowds in front of bottlenecks
                      from the perspective of physics and social psychology,
                      2018.},
      month         = {Dec},
      date          = {2023-12-07},
      organization  = {2023 the 3rd International Conference
                       on Computers and Automation, Paris
                       (France), 7 Dec 2023 - 9 Dec 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-2023-05241},
      url          = {https://juser.fz-juelich.de/record/1019197},
}