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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},
}