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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Main Author: | |
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Format: | Electronic Book Chapter |
Published: |
KIT Scientific Publishing
2016
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Series: | Forschungsberichte aus der Industriellen Informationstechnik / Institut für Industrielle Informationstechnik (IIIT), Karlsruher Institut für Technologie
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Subjects: | |
Online Access: | DOAB: download the publication DOAB: description of the publication |
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Summary: | 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. |
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Physical Description: | 1 electronic resource (XI, 178 p. p.) |
ISBN: | KSP/1000054659 9783731505297 |
Access: | Open Access |