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@INPROCEEDINGS{Alia:916435,
      author       = {Alia, Ahmed and Maree, Mohammed and Chraibi, Mohcine},
      title        = {{A} {F}ast {H}ybrid {D}eep {N}eural {N}etwork {M}odel for
                      pushing behavior detection in human crowds},
      publisher    = {IEEE},
      reportid     = {FZJ-2022-06229},
      pages        = {1-2},
      year         = {2023},
      abstract     = {Deep learning technology is regarded as one of the latest
                      advances in data science and analytics due to its learning
                      abilities from the data. As a result, deep learning is
                      widely applied in the human crowd analysis domain. Although
                      it has achieved remarkable success in this area, a fast and
                      robust model for pushing behavior detection in the human
                      crowd is unavailable. This paper proposes a model that
                      allows crowd-monitoring systems to detect pushing behavior
                      early, helping organizers make timely decisions before
                      dangerous situations appear. This particularly becomes more
                      challenging when applied to real-time video streams of
                      crowded events, which the proposed model accomplishes with
                      reasonable time latency. To achieve this, the model employs
                      a hybrid deep neural network.},
      month         = {Dec},
      date          = {2022-12-05},
      organization  = {2022 IEEE/ACS 19th International
                       Conference on Computer Systems and
                       Applications, Zayed University, Abu
                       Dhabi (U Arab Emirates), 5 Dec 2022 - 7
                       Dec 2022},
      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)8},
      UT           = {WOS:000932894200052},
      doi          = {10.1109/AICCSA56895.2022.10017883},
      url          = {https://juser.fz-juelich.de/record/916435},
}