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@INPROCEEDINGS{Alia:903465,
      author       = {Alia, Ahmed and Maree, Mohammed and Haensel, David and
                      Chraibi, Mohcine and Lügering, Helena and Sieben, Anna and
                      Üsten, Ezel},
      title        = {{T}wo {M}ethods for {D}etecting {P}ushing {B}ehavior from
                      {V}ideos: {A} {P}sychological {R}ating {S}ystem and a {D}eep
                      {L}earning-based {A}pproach},
      reportid     = {FZJ-2021-05138},
      year         = {2021},
      abstract     = {In crowded entrances, some people try to be faster and
                      therefore start pushing others. This pushing
                      behaviorpotentially increases density, and decreases comfort
                      as well safety of events. From research and practical
                      perspectives, it is interesting to know where, why, and when
                      pushing appears and, thereby, to understand the
                      heterogeneity of movements in crowds. This paper presents
                      two methods for identifying pushing in videos of crowds. The
                      first one is a newly developed psychological rating system.
                      It categorizes forward motion of people into four classes:
                      1) falling behind, 2) just walking, 3) mild pushing, and 4)
                      strong pushing. The rating is performed by trained human
                      observersusing the software PeTrack. This procedure allows
                      to annotate individual behavior in every second, resulting
                      in a high time resolution. However, this approach is
                      time-consuming. The second method is an automated tool that
                      can assist ad-hoc recognition of pushing behavior. We
                      propose a novel deep learning-based technique that
                      automatically detects pushing behavior scenarios from
                      videos. In particular, we combine deep optical flow
                      information with wheel visualization techniques to extract
                      useful motion features from video sequences and generate a
                      motion feature map between each two consecutive frames that
                      visualizes: the motion speed, motion direction, spaces in
                      crowd and interactions between pedestrians. Then,
                      convolutional neural networks are used to extract the most
                      relevant features (deep features) from these maps.
                      Afterwards, additional supervised convolutional neural
                      networks are used to automatically learn from the deep
                      features to classify frames into pushing or non-pushing
                      behavior classes. To evaluate this approach, we have
                      conducted experiments using manually annotated videos by the
                      first method. Results demonstrated a high congruence of both
                      approaches and a promising performance in identifying
                      pushing behavior from videos.},
      month         = {Nov},
      date          = {2021-11-29},
      organization  = {10th Pedestrian and Evacuation
                       Dynamics Conference, Melbourne $\&$
                       Sydney (Online) (Australia), 29 Nov
                       2021 - 30 Nov 2021},
      subtyp        = {Invited},
      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)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/903465},
}