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| 001 | 187743 | ||
| 005 | 20220930130038.0 | ||
| 020 | _ | _ | |a 978-3-95806-025-8 |
| 024 | 7 | _ | |2 Handle |a 2128/8402 |
| 024 | 7 | _ | |2 URN |a urn:nbn:de:0001-2015011609 |
| 024 | 7 | _ | |2 ISSN |a 1868-8489 |
| 037 | _ | _ | |a FZJ-2015-01334 |
| 041 | _ | _ | |a German |
| 100 | 1 | _ | |0 P:(DE-Juel1)132064 |a Boltes, Maik |b 0 |e Corresponding Author |g male |u fzj |
| 245 | _ | _ | |a Automatische Erfassung präziser Trajektorien in Personenströmen hoher Dichte |f 2014-10-24 |
| 260 | _ | _ | |a Jülich |b Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag |c 2015 |
| 300 | _ | _ | |a xii, 308 S. |
| 336 | 7 | _ | |0 PUB:(DE-HGF)11 |2 PUB:(DE-HGF) |a Dissertation / PhD Thesis |b phd |m phd |s 1424254750_8546 |
| 336 | 7 | _ | |0 2 |2 EndNote |a Thesis |
| 336 | 7 | _ | |2 DRIVER |a doctoralThesis |
| 336 | 7 | _ | |2 BibTeX |a PHDTHESIS |
| 336 | 7 | _ | |2 DataCite |a Output Types/Dissertation |
| 336 | 7 | _ | |2 ORCID |a DISSERTATION |
| 490 | 0 | _ | |a Schriften des Forschungszentrums Jülich. IAS Series |v 27 |
| 502 | _ | _ | |a Universität Köln, Diss., 2014 |b Dr. |c Universität Köln |d 2014 |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |0 G:(DE-HGF)POF3-511 |a 511 - Computational Science and Mathematical Methods (POF3-511) |c POF3-511 |f POF III |x 0 |
| 650 | _ | 7 | |0 V:(DE-588b)4012494-0 |2 GND |a Dissertation |x Diss. |
| 773 | _ | _ | |y 2015 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/187743/files/FZJ-2015-01334.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:187743 |p openaire |p open_access |p urn |p driver |p VDB |p dnbdelivery |
| 910 | 1 | _ | |0 I:(DE-588b)5008462-8 |6 P:(DE-Juel1)132064 |a Forschungszentrum Jülich GmbH |b 0 |k FZJ |
| 913 | 0 | _ | |0 G:(DE-HGF)POF2-411 |1 G:(DE-HGF)POF2-410 |2 G:(DE-HGF)POF2-400 |a DE-HGF |b Schlüsseltechnologien |l Supercomputing |v Computational Science and Mathematical Methods |x 0 |
| 913 | 1 | _ | |0 G:(DE-HGF)POF3-511 |1 G:(DE-HGF)POF3-510 |2 G:(DE-HGF)POF3-500 |3 G:(DE-HGF)POF3 |4 G:(DE-HGF)POF |a DE-HGF |b Key Technologies |l Supercomputing & Big Data |v Computational Science and Mathematical Methods |x 0 |
| 914 | 1 | _ | |y 2015 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0510 |2 StatID |a OpenAccess |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a phd |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a FullTexts |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | 1 | _ | |a FullTexts |
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