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dc.contributor.authorBecker, Stefan
dc.date.accessioned2021-02-17T16:38:35Z
dc.date.available2021-02-17T16:38:35Z
dc.date.issued2020
dc.identifierONIX_20210217_9783731510383_3
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/46859
dc.description.abstractThis work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together.
dc.languageEnglish
dc.relation.ispartofseriesKarlsruher Schriften zur Anthropomatik
dc.subject.classificationthema EDItEUR::U Computing and Information Technology::UY Computer scienceen_US
dc.subject.othervideobasierte Objektverfolgung
dc.subject.otherstate estimation
dc.subject.othervisual tracking
dc.subject.othertrajectory prediction
dc.subject.otherTrajektorienpradiktion
dc.subject.otherZustandsschatzung
dc.titleDynamic Switching State Systems for Visual Tracking
dc.typebook
oapen.identifier.doi10.5445/KSP/1000122541
oapen.relation.isPublishedBy44e29711-8d53-496b-85cc-3d10c9469be9
oapen.series.number50
oapen.pages228
oapen.place.publicationKarlsruhe


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