000891131 001__ 891131
000891131 005__ 20220930130310.0
000891131 0247_ $$2doi$$a10.3390/s21062108
000891131 0247_ $$2Handle$$a2128/27448
000891131 0247_ $$2pmid$$a33802810
000891131 0247_ $$2WOS$$aWOS:000652727400001
000891131 037__ $$aFZJ-2021-01383
000891131 041__ $$aEnglish
000891131 082__ $$a620
000891131 1001_ $$0P:(DE-Juel1)132064$$aBoltes, Maik$$b0$$eCorresponding author$$ufzj
000891131 245__ $$aA Hybrid Tracking System of Full-Body Motion Inside Crowds
000891131 260__ $$aBasel$$bMDPI$$c2021
000891131 3367_ $$2DRIVER$$aarticle
000891131 3367_ $$2DataCite$$aOutput Types/Journal article
000891131 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1641839516_21106
000891131 3367_ $$2BibTeX$$aARTICLE
000891131 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000891131 3367_ $$00$$2EndNote$$aJournal Article
000891131 520__ $$aFor our understanding of the dynamics inside crowds, reliable empirical data are needed, which could enable increases in safety and comfort for pedestrians and the design of models reflecting the real dynamics. A well-calibrated camera system can extract absolute head position with high accuracy. The inclusion of inertial sensors or even self-contained full-body motion capturing systems allows the relative tracking of invisible people or body parts or capturing the locomotion of the whole body even in dense crowds. The newly introduced hybrid system maps the trajectory of the top of the head coming from a full-body motion tracking system to the head trajectory of a camera system in global space. The fused data enable the analysis of possible correlations of all observables. In this paper we present an experiment of people passing though a bottleneck and show by example the influences of bottleneck width and motivation on the overall movement, velocity, stepping locomotion and rotation of the pelvis. The hybrid tracking system opens up new possibilities for analyzing pedestrian dynamics inside crowds, such as the space requirement while passing through a bottleneck. The system allows linking any body motion to characteristics describing the situation of a person inside a crowd, such as the density or movements of other participants nearby.
000891131 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
000891131 536__ $$0G:(DE-Juel-1)BMBF-DB001534$$aCroma - Crowd Management in Transport Infrastructures (BMBF-DB001534)$$cBMBF-DB001534$$x1
000891131 7001_ $$0P:(DE-Juel1)173971$$aAdrian, Juliane$$b1$$ufzj
000891131 7001_ $$0P:(DE-Juel1)177932$$aRaytarowski, Anna-Katharina$$b2$$ufzj
000891131 770__ $$aNovel Applications of Positioning Systems and Sensors
000891131 773__ $$0PERI:(DE-600)2052857-7$$a10.3390/s21062108$$n6$$p2108$$tSensors$$v21$$x1424-8220$$y2021
000891131 8564_ $$uhttps://juser.fz-juelich.de/record/891131/files/Invoice_MDPI_sensors-1079958_1536.24EUR.pdf
000891131 8564_ $$uhttps://juser.fz-juelich.de/record/891131/files/sensors-21-02108.pdf$$yOpenAccess
000891131 8767_ $$8sensors-1079958$$92021-03-12$$d2021-03-24$$eAPC$$jZahlung erfolgt$$psensors-1079958$$zFZJ-2021-01349, Belegnr. 1200164835 / 2021
000891131 909CO $$ooai:juser.fz-juelich.de:891131$$popenCost$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire$$pdnbdelivery
000891131 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132064$$aForschungszentrum Jülich$$b0$$kFZJ
000891131 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)173971$$aForschungszentrum Jülich$$b1$$kFZJ
000891131 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)177932$$aForschungszentrum Jülich$$b2$$kFZJ
000891131 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
000891131 9130_ $$0G:(DE-HGF)POF3-511$$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
000891131 9141_ $$y2021
000891131 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2020-09-08
000891131 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2020-09-08
000891131 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000891131 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2020-09-08
000891131 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bSENSORS-BASEL : 2018$$d2020-09-08
000891131 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2020-09-08
000891131 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2020-09-08
000891131 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2020-09-08
000891131 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2020-09-08
000891131 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2020-09-08
000891131 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2020-09-08
000891131 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000891131 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2020-09-08
000891131 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2020-09-08
000891131 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2020-09-08
000891131 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2020-09-08
000891131 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2020-09-08
000891131 920__ $$lyes
000891131 9201_ $$0I:(DE-Juel1)IAS-7-20180321$$kIAS-7$$lZivile Sicherheitsforschung$$x0
000891131 980__ $$ajournal
000891131 980__ $$aVDB
000891131 980__ $$aI:(DE-Juel1)IAS-7-20180321
000891131 980__ $$aAPC
000891131 980__ $$aUNRESTRICTED
000891131 9801_ $$aAPC
000891131 9801_ $$aFullTexts