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@ARTICLE{Alia:907874,
      author       = {Alia, Ahmed and Maree, Mohammed and Chraibi, Mohcine},
      title        = {{A} {H}ybrid {D}eep {L}earning and {V}isualization
                      {F}ramework for {P}ushing {B}ehavior {D}etection in
                      {P}edestrian {D}ynamics},
      journal      = {Sensors},
      volume       = {22},
      number       = {11},
      issn         = {1424-8220},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2022-02263},
      pages        = {4040},
      year         = {2022},
      note         = {Pilotprojekt zur Entwicklung eines
                      palästinensisch-deutschen Forschungs- und
                      Promotionsprogramms 'Palestinian-German Science Bridge'
                      (01DH16027) (01DH16027)"},
      abstract     = {Crowded event entrances could threaten the comfort and
                      safety of pedestrians, especially when some pedestrians push
                      others or use gaps in crowds to gain faster access to an
                      event. Studying and understanding pushing dynamics leads to
                      designing and building more comfortable and safe entrances.
                      Researchers—to understand pushing dynamics—observe and
                      analyze recorded videos to manually identify when and where
                      pushing behavior occurs. Despite the accuracy of the manual
                      method, it can still be time-consuming, tedious, and hard to
                      identify pushing behavior in some scenarios. In this
                      article, we propose a hybrid deep learning and visualization
                      framework that aims to assist researchers in automatically
                      identifying pushing behavior in videos. The proposed
                      framework comprises two main components: (i) Deep optical
                      flow and wheel visualization; to generate motion information
                      maps. (ii) A combination of an EfficientNet-B0-based
                      classifier and a false reduction algorithm for detecting
                      pushing behavior at the video patch level. In addition to
                      the framework, we present a new patch-based approach to
                      enlarge the data and alleviate the class imbalance problem
                      in small-scale pushing behavior datasets. Experimental
                      results (using real-world ground truth of pushing behavior
                      videos) demonstrate that the proposed framework achieves an
                      $86\%$ accuracy rate. Moreover, the EfficientNet-B0-based
                      classifier outperforms baseline CNN-based classifiers in
                      terms of accuracy.},
      cin          = {IAS-7},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IAS-7-20180321},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35684663},
      UT           = {WOS:000808626000001},
      doi          = {10.3390/s22114040},
      url          = {https://juser.fz-juelich.de/record/907874},
}