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AuthorMeißner, Daniel Alexanderdc.contributor.author
Date of accession2016-03-15T10:40:11Zdc.date.accessioned
Available in OPARU since2016-03-15T10:40:11Zdc.date.available
Year of creation2016dc.date.created
AbstractDue to an increasing number of traffic accident fatalities at intersections, the prevention of traffic accidents at intersection attracts more and more attention in public as well as in research. Thus, a busy public intersection in Aschaffenburg, Germany was equipped with an intersection perception system, which contains multiple laserscanners and video cameras as well as data processing units. The focus of this thesis lays on the tracking of road users at traffic intersections. Thus, a tracking approach is required which is able to handle multiple objects detected by multiple classifying sensors with different field of views. Due to the topdown modeling of the multi-object tracking problem and the availability of efficient implementations, a Gaussian mixture probability hypothesis density (GM-PHD) filter is used. Based on this the classifying multiple-model probability hypothesis density (CMMPHD) filter is developed which facilitates to track road users of different type with its appropriate motion model and classify them. The classification is able to distinguish pedestrians, bikes, cars, and trucks. Therefore, features of the measurements and of the tracks are used. To model the object class of each track a basic belief assignment (BBA) of the Dempster-Shafer theory of evidence (DST) is exploited. Based on the estimated objects’ classes the objects’ motion model transition probabilities of the multiple model filter are adapted. To reliably recognise the road users in their varying appearances, laserscanners and video cameras are used. The fusion of the sensor measurements with the multi-sensor CMMPHD filter is realized by the introduction of the sensor individual modeling of the recognition abilities and the detection probability. In the end, the developed methods to recognize and track road users at traffic intersections are evaluated with real-world sensor and reference data.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandard (ohne Print-On-Demand)dc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_opod_v1dc.rights.uri
KeywordIntersection perceptiondc.subject
KeywordMulti-object trackingdc.subject
KeywordMulti-sensor fusiondc.subject
KeywordPHD filterdc.subject
KeywordRandom finite setsdc.subject
Dewey Decimal GroupDDC 620 / Engineering & allied operationsdc.subject.ddc
LCSHDempster-Shafer theory of evidencedc.subject.lcsh
LCSHScanning systemsdc.subject.lcsh
LCSHTraffic safetydc.subject.lcsh
TitleIntersection-based road user tracking using a classifying multiple-model PHD filterdc.title
Resource typeDissertationdc.type
DOIhttp://dx.doi.org/10.18725/OPARU-3257dc.identifier.doi
PPN847528553dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-vts-99026dc.identifier.urn
GNDDatenfusiondc.subject.gnd
GNDDempster-Shafer-Theoriedc.subject.gnd
GNDLaserscannerdc.subject.gnd
GNDMultisensordc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften und Informatikuulm.affiliationGeneral
Date of activation2016-02-03T17:12:25Zuulm.freischaltungVTS
Peer reviewneinuulm.peerReview
Shelfmark print versionW: W-H 14.637uulm.shelfmark
DCMI TypeTextuulm.typeDCMI
VTS-ID9902uulm.vtsID
CategoryPublikationenuulm.category
University Bibliographyjauulm.unibibliographie


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