Dynamic Switching State Systems for Visual Tracking

dc.contributor.author	Becker, Stefan
dc.date.accessioned	2021-02-18T03:02:08Z
dc.date.available	2021-02-18T03:02:08Z
dc.date.issued	2020
dc.date.submitted	2021-02-17T16:38:35Z
dc.identifier	ONIX_20210217_9783731510383_3
dc.identifier	https://library.oapen.org/handle/20.500.12657/46859
dc.identifier.uri	https://directory.doabooks.org/handle/20.500.12854/63764
dc.description.abstract	This 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.language	English
dc.relation.ispartofseries	Karlsruher Schriften zur Anthropomatik
dc.rights	open access
dc.subject.other	videobasierte Objektverfolgung
dc.subject.other	state estimation
dc.subject.other	visual tracking
dc.subject.other	trajectory prediction
dc.subject.other	Trajektorienpradiktion
dc.subject.other	Zustandsschatzung
dc.subject.other	thema EDItEUR::U Computing and Information Technology::UY Computer science
dc.title	Dynamic Switching State Systems for Visual Tracking
dc.type	book
oapen.identifier.doi	10.5445/KSP/1000122541
oapen.relation.isPublishedBy	68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2
oapen.pages	228
oapen.place.publication	Karlsruhe
dc.seriesnumber	50

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