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Objektsensitive Verfolgung und Klassifikation von Fußgängern mit verteilten Multi-Sensor-Trägern

State estimation of an unknown number of objects remains a challenging topic - despite the existence of theoretically bayes-optimal multi-object-filters - due to numerous assumptions in the modeling process. This thesis evaluates such filters in real multi-object-multi-sensor scenarios and proposes...

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Gorde:
Xehetasun bibliografikoak
Egile nagusia: Pallauf, Johannes (auth)
Formatua: Baliabide elektronikoa Liburu kapitulua
Argitaratua: KIT Scientific Publishing 2016
Saila:Forschungsberichte aus der Industriellen Informationstechnik / Institut für Industrielle Informationstechnik (IIIT), Karlsruher Institut für Technologie
Gaiak:
Technology: general issues
Multi-Objekt-Verfolgung
verteilte Systeme
SensorenMulti-object-tracking
sensors
Objektklassifikation
object classification
pedestrian tracking
distributed systems
Personenverfolgung
Sarrera elektronikoa:DOAB: download the publication
DOAB: description of the publication
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Deskribapena
Gaia:State estimation of an unknown number of objects remains a challenging topic - despite the existence of theoretically bayes-optimal multi-object-filters - due to numerous assumptions in the modeling process. This thesis evaluates such filters in real multi-object-multi-sensor scenarios and proposes necessary extensions to existing models. The main application of the thesis is indoor pedestrian tracking.
Deskribapen fisikoa:1 electronic resource (XI, 178 p. p.)
ISBN:KSP/1000054659
9783731505297
Sartu:Open Access

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