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@INPROCEEDINGS{Boomers:1026672,
      author       = {Boomers, Ann Katrin and Boltes, Maik},
      title        = {{S}houlder {R}otation {M}easurement in {C}amera and 3{D}
                      {M}otion {C}apturing {D}ata},
      volume       = {443},
      address      = {Singapore},
      publisher    = {Springer Nature Singapore},
      reportid     = {FZJ-2024-03490},
      isbn         = {978-981-99-7975-2 (print)},
      series       = {Lecture Notes in Civil Engineering},
      pages        = {35 - 42},
      year         = {2024},
      note         = {Missing Journal: = 2366-2565 (import from CrossRef Book
                      Series, Journals: juser.fz-juelich.de)},
      comment      = {Traffic and Granular Flow '22 / Rao, K. Ramachandra
                      (Editor) ; Singapore : Springer Nature Singapore, 2024,
                      Chapter 5 ; ISSN: 2366-2557=2366-2565 ; ISBN:
                      978-981-99-7975-2=978-981-99-7976-9 ;
                      doi:10.1007/978-981-99-7976-9},
      booktitle     = {Traffic and Granular Flow '22 / Rao,
                       K. Ramachandra (Editor) ; Singapore :
                       Springer Nature Singapore, 2024,
                       Chapter 5 ; ISSN: 2366-2557=2366-2565 ;
                       ISBN:
                       978-981-99-7975-2=978-981-99-7976-9 ;
                       doi:10.1007/978-981-99-7976-9},
      abstract     = {The individual movement of pedestrians and their body
                      parts, as for example shoulders, is of great interest to
                      understand body movement and interactions and thus to
                      improve pedestrian models. Nearly all laboratory experiments
                      in pedestrian dynamics use camera data to obtain
                      trajectories. A perpendicular top view of the camera does
                      not only allow to extract the head position but also data of
                      upper body segments. The detection is more reliable if
                      shoulders are tagged with markers and for low densities of
                      people. In this study a head-shoulder model is used to
                      assign coloured shoulder markers to a person. The location
                      of a marker is predicted by taking head position, basic body
                      dimensions, movement direction and camera angle into
                      account. It is implemented as a new feature in the software
                      PeTrack. This paper shows a comparison of shoulder rotation
                      measurements obtained from 3D motion capturing systems
                      (Xsens) with those from camera data using the newly
                      introduced model and detection technique. Detection rates
                      and limits of the camera-based rotation measurement are
                      shown and implications are given for the future application
                      at high densities in crowds.},
      month         = {Oct},
      date          = {2022-10-15},
      organization  = {Traffic and Granular Flow '22, New
                       Delhi (India), 15 Oct 2022 - 17 Oct
                       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)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      UT           = {WOS:001283974700005},
      doi          = {10.1007/978-981-99-7976-9_5},
      url          = {https://juser.fz-juelich.de/record/1026672},
}