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@PHDTHESIS{Boltes:187743,
      author       = {Boltes, Maik},
      title        = {{A}utomatische {E}rfassung präziser {T}rajektorien in
                      {P}ersonenströmen hoher {D}ichte},
      volume       = {27},
      school       = {Universität Köln},
      type         = {Dr.},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2015-01334},
      isbn         = {978-3-95806-025-8},
      series       = {Schriften des Forschungszentrums Jülich. IAS Series},
      pages        = {xii, 308 S.},
      year         = {2015},
      note         = {Universität Köln, Diss., 2014},
      abstract     = {Simulations can help make facilities for pedestrians safer
                      and more comfortable. A proper understanding of crowd
                      dynamics is essential to developing reliable models for such
                      simulations. Detailed and reproducible datasets of real
                      crowd movements are needed for analysis and modelling. Such
                      datasets are also required for later calibration and
                      validation of said models. This thesis describes the
                      collection of such data from overhead video recordings.
                      Individual trajectories are extracted and make it possible
                      to obtain the most relevant quantities of the dynamic e.g.
                      pedestrian density, velocity and flow. Traffic jams and
                      other high density situations are of special interest since
                      this is where critical situations are to be expected.
                      Therefor the developed methods have to also reliable extract
                      an individual’s movement in such situations. The movement
                      of pedestrians is affected by many factors such as geometry,
                      crowd density, motivation and culture. To investigate these
                      numerous influences a large number of experiments with a
                      huge number of participants have been carried out. The
                      automatic extraction of the trajectories provides a
                      significant advantage compared to manual methods in terms of
                      the time required, accuracy and reproducibility. The
                      extraction process consists of the image calibration
                      followed by the detection, tracking and determination of the
                      real world position of all individuals. For the detection of
                      a person various markers and corresponding extraction
                      techniques have been developed for the different
                      applications and local conditions. A markerless method was
                      also developed, which is especially useful for field
                      studies. Through the use of stereo cameras high detection
                      rates were achieved without markers, even in high density
                      situations. All developments regarding the extraction
                      process have been integrated into the software PeTrack. To
                      enable a deeper understanding of the results the technical
                      aspects of the trajectory collection are described in
                      addition to the recognition techniques.},
      keywords     = {Dissertation (GND)},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511)},
      pid          = {G:(DE-HGF)POF3-511},
      typ          = {PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2015011609},
      url          = {https://juser.fz-juelich.de/record/187743},
}