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DOI: 10.5445/IR/1000088877
Veröffentlicht am 21.12.2018

Interactive and Quantitative Knowledge-Discovery in Large-Scale 3D Tracking Data

Schott, Benjamin

Abstract:
New developments in tracking technologies combined with enhanced data
storage techniques provide powerful ways to collect a tremendous amount of
highly resolved object localization data represented as trajectories.
Due to the enormous size and complexity of the routinely produced datasets, the systematic analysis and the extraction of relevant knowledge out of the data is a challenging problem. Furthermore, available prior knowledge could be utilized for sophisticated analysis can in many cases not be sufficiently considered for such complex datasets, due to the technical limitations of existing analysis approaches and software tools. Moreover, existing state-of-the-art tracking algorithms are not able to create error-free tracks in the presence of highly dense and noisy data measurements leading to crucial problems coping with the fragmented tracking data.
The major contributions of the present thesis are a new concept to
systematically incorporate prior knowledge in the knowledge discovery process of large-scale tracking data combining interactive visual exploration and automated trajectory analysis methods. In addition, a ... mehr


Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Hochschulschrift
Jahr 2018
Sprache Englisch
Identifikator URN: urn:nbn:de:swb:90-888777
KITopen-ID: 1000088877
HGF-Programm 47.01.02 (POF III, LK 01)
Verlag Karlsruhe
Umfang XI, 263 S.
Abschlussart Dissertation
Fakultät Fakultät für Maschinenbau (MACH)
Institut Institut für Automation und angewandte Informatik (IAI)
Prüfungsdatum 17.12.2018
Referent/Betreuer Prof. R. Mikut
Schlagworte Tracking Analyse
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