000842764 001__ 842764
000842764 005__ 20210129232448.0
000842764 0247_ $$2doi$$a10.1109/AVSS.2017.8078471
000842764 0247_ $$2Handle$$a2128/16916
000842764 037__ $$aFZJ-2018-00964
000842764 1001_ $$0P:(DE-Juel1)132064$$aBoltes, Maik$$b0$$eCorresponding author
000842764 1112_ $$a2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance$$cLecce$$d2017-08-29 - 2017-09-01$$gAVSS$$wItaly
000842764 245__ $$aGathering of data under laboratory conditions for the deep analysis of pedestrian dynamics in crowds
000842764 260__ $$bIEEE$$c2017
000842764 300__ $$a1-6
000842764 3367_ $$2ORCID$$aCONFERENCE_PAPER
000842764 3367_ $$033$$2EndNote$$aConference Paper
000842764 3367_ $$2BibTeX$$aINPROCEEDINGS
000842764 3367_ $$2DRIVER$$aconferenceObject
000842764 3367_ $$2DataCite$$aOutput Types/Conference Paper
000842764 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1517317824_10558
000842764 520__ $$aFor the understanding of the dynamics inside crowds reliable empirical data are needed. On that basis the safety and comfort for pedestrians can be increased and models reflecting the real dynamics can be designed.For that purpose we are developing the free framework PeTrack collecting data from laboratory experiments. With the new integration of the detection of individual codes the presented framework is able to personalize every single trajectory by static information of each participant. The inclusion of inertial sensors allows the tracking of invisible people and capturing the locomotion of the whole body also in dense crowds. Fused information enables the analysis of possible correlations of all observables and thus finding the main influencing parameters for different situations.
000842764 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000842764 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x1
000842764 588__ $$aDataset connected to CrossRef Conference
000842764 7001_ $$0P:(DE-Juel1)142414$$aSchumann, Jette$$b1$$ufzj
000842764 7001_ $$0P:(DE-Juel1)7386$$aSalden, Daniel$$b2
000842764 773__ $$a10.1109/AVSS.2017.8078471
000842764 8564_ $$uhttps://juser.fz-juelich.de/record/842764/files/08078471.pdf$$yOpenAccess
000842764 8564_ $$uhttps://juser.fz-juelich.de/record/842764/files/08078471.gif?subformat=icon$$xicon$$yOpenAccess
000842764 8564_ $$uhttps://juser.fz-juelich.de/record/842764/files/08078471.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess
000842764 8564_ $$uhttps://juser.fz-juelich.de/record/842764/files/08078471.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
000842764 8564_ $$uhttps://juser.fz-juelich.de/record/842764/files/08078471.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
000842764 8564_ $$uhttps://juser.fz-juelich.de/record/842764/files/08078471.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000842764 909CO $$ooai:juser.fz-juelich.de:842764$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000842764 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132064$$aForschungszentrum Jülich$$b0$$kFZJ
000842764 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)142414$$aForschungszentrum Jülich$$b1$$kFZJ
000842764 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000842764 9141_ $$y2017
000842764 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000842764 920__ $$lyes
000842764 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000842764 980__ $$acontrib
000842764 980__ $$aVDB
000842764 980__ $$aUNRESTRICTED
000842764 980__ $$aI:(DE-Juel1)JSC-20090406
000842764 9801_ $$aFullTexts