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  • Molecular Diversity Preservation International  (2)
  • Hannover : Leibniz Universität Hannover  (1)
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  • 1
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    Series available for loan
    Erfassung von Trajektorien und Erkennung von Bewegungsmustern (2018)
    Hannover : Leibniz Universität Hannover
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    Call number: S 99.0139(340)
    In: Wissenschaftliche Arbeiten der Fachrichtung Vermessungswesen der Universität Hannover, Nr. 340
    Type of Medium: Series available for loan
    Pages: 170 Seiten , Illustrationen, Diagramme
    ISSN: 0174-1454
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Vermessungswesen der Universität Hannover Nr. 340
    URL: https://d-nb.info/1165014033/04
    Language: German
    Note: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2018 , 1 Einleitung 1.1 Hintergrund und Problemstellung 1.2 Ziel der Arbeit 1.2.1 Problemstellung: Erfassung von Trajektorien 1.2.2 Problemstellung: Erkennung von Bewegungsmustern in Trajektorien 1.3 Gliederung 2 Grundlagen 2.1 Modellierung von Objektbewegungen 2.2 Erfassung von Trajektorien 2.2.1 GNSS-Tracking 2.2.2 Videobasiertes Tracking 2.2.3 Vergleich des GNSS- und videobasierten Trackings 2.2.4 Weitere Tracking-Verfahren 2.2.5 Probabilistische Modellierung 2.2.6 Viterbi-Algorithmus 2.3 Erkennung von Bewegungsmustern 2.3.1 Data Mining 2.3.2 Filterung und Glättung 2.3.3 Segmentierung 2.3.4 Distanzmaße zur Bestimmung der Ähnlichkeit von Trajektorien 2.3.5 Maschinelles Lernen im Kontext raum-zeitlicher Daten 2.3.6 Sequenzmustererkennung 2.4 Dynamische Programmierung 2.5 Unterschiedliche Varianten der Datenverarbeitung 2.5.1 Zentrale und dezentrale Verarbeitung 2.5.2 Informationsaustausch 3 Stand der Forschung und verwandte Arbeiten 3.1 Erfassung von Trajektorien 3.1.1 Objektdetektion 3.1.2 Objekt-Tracking 3.1.3 Fusion heterogener Detektionen 3.1.4 Kommerzielle Systeme 3.1.5 Diskussion und Fazit 3.2 Mustererkennung in Trajektorien 3.2.1 Erkennung von wiederkehrenden unbekannten Mustern 3.2.2 Diskussion und Fazit 4 Erfassung von Trajektorien - GPS-unterstütztes Kamera-Tracking 4.1 Überblick über den Lösungsansatz 4.2 Sensoren und Eingangsdaten 4.2.1 GPS-Daten 4.2.2 Kameradaten 4.3 Vorverarbeitung 4.4 Fusion der heterogenen Daten 4.4.1 Detektionsbasierte Modellierung 4.4.2 Rasterbasierte Modellierung 4.4.3 Generierung der Trajektorien 4.5 Laufzeit des Algorithmus 4.6 System design 5 Mustererkennung in Trajektorien 5.1 Definition von Bewegungsmustern 5.2 Überblick über das entwickelte Mustererkennungsverfahren 5.3 Trajektorien als Datengrundlage 5.4 Vorverarbeitung 5.4.1 Datenbereinigung 5.4.2 Datenselektion 5.4.3 Datenintegration und Transformation 5.5 Mustererkennung: Clustering-basierter Ansatz 5.5.1 Segmentierung der Trajektorien 5.5.2 Clustering der Trajektorien 5.6 Mustererkennung: Sequenzbasierter Ansatz 5.6.1 Eingangsdaten 5.6.2 Generierung der Sequenzen aus Bewegungen 5.6.3 Bestimmung des Alphabets 5.6.4 Identifikation wiederkehrender Teilsequenzen 5.6.5 Rücktransformation zu Trajektorien 5.7 Laufzeit des Algorithmus 6 Experimente und Evaluation der Erfassung der Trajektorien 6.1 Verwendete Software 6.2 Verwendete Sensoren 6.3 Korrektheit der Zuordnungen 6.3.1 Experiment: 2 Personen 6.3.2 Experiment: Fußballanalyse 6.4 Geometrische Genauigkeit der Trajektorien 6.5 Laufzeit 6.6 Fazit 7 Experimente und Evaluation der Mustererkennung 7.1 Verwendete Software 7.2 Ergebnisverifikation 7.3 Interessantheitsmaß für Bewegungsmuster 7.4 Parameterstudien 7.4.1 Eingabeparameter 7.4.2 Datendichte 7.4.3 Invarianzen 7.5 Experimente auf realen Datensätzen 7.5.1 Beschreibung der Datensätze und Experimente 7.5.2 Experiment 1 - ACM DEBS 2013-Datensatz 7.5.3 Experiment 2 - GPS-Fußball-Datensatz 7.5.4 Experiment 3 - MapConstruction.org 7.5.5 Experiment 4 - Mantelpaviane 8 Zusammenfassung und Ausblick 8.1 Zusammenfassung 8.2 Ausblick Abbildungsverzeichnis Tabellenverzeichnis Literaturverzeichnis Lebenslauf Danksagung
    Location: Lower compact magazine
    Branch Library: GFZ Library
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  • 2
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    Geological Map Generalization Driven by Size Constraints (2020)
    Molecular Diversity Preservation International
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    Publication Date: 2020-04-24
    Description: Geological maps are an important information source used in the support of activities relating to mining, earth resources, hazards, and environmental studies. Owing to the complexity of this particular map type, the process of geological map generalization has not been comprehensively addressed, and thus a complete automated system for geological map generalization is not yet available. In particular, while in other areas of map generalization constraint-based techniques have become the prevailing approach in the past two decades, generalization methods for geological maps have rarely adopted this approach. This paper seeks to fill this gap by presenting a methodology for the automation of geological map generalization that builds on size constraints (i.e., constraints that deal with the minimum area and distance relations in individual or pairs of map features). The methodology starts by modeling relevant size constraints and then uses a workflow consisting of generalization operators that respond to violations of size constraints (elimination/selection, enlargement, aggregation, and displacement) as well as algorithms to implement these operators. We show that the automation of geological map generalization is possible using constraint-based modeling, leading to improved process control compared to current approaches. However, we also show the limitations of an approach that is solely based on size constraints and identify extensions for a more complete workflow.
    Electronic ISSN: 2220-9964
    Topics: Architecture, Civil Engineering, Surveying , Geosciences
    Published by Molecular Diversity Preservation International
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  • 3
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    Assessing Older Adults’ Daily Mobility: A Comparison of GPS-Derived and Self-Reported Mobility Indicators (2019)
    Molecular Diversity Preservation International
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    Publication Date: 2019-10-19
    Description: Interest in global positioning system (GPS)-based mobility assessment for health and aging research is growing, and with it the demand for validated GPS-based mobility indicators. Time out of home (TOH) and number of activity locations (#ALs) are two indicators that are often derived from GPS data, despite lacking consensus regarding thresholds to be used to extract those as well as limited knowledge about their validity. Using 7 days of GPS and diary data of 35 older adults, we make the following three main contributions. First, we perform a sensitivity analysis to investigate how using spatial and temporal thresholds to compute TOH and #ALs affects the agreement between self-reported and GPS-based indicators. Second, we show how daily self-reported and GPS-derived mobility indicators are compared. Third, we explore whether the type and duration of self-reported activity events are related to the degree of correspondence between reported and GPS event. Highest indicator agreement was found for temporal interpolation (Tmax) of up to 5 h for both indicators, a radius (Dmax) to delineate home between 100 and 200 m for TOH, and for #ALs a spatial extent (Dmax) between 125 and 200 m, and temporal extent (Tmin) between 5 and 6 min to define an activity location. High agreement between self-reported and GPS-based indicators is obtained for TOH and moderate agreement for #ALs. While reported event type and duration impact on whether a reported event has a matching GPS event, indoor and outdoor events are detected at equal proportions. This work will help future studies to choose optimal threshold settings and will provide knowledge about the validity of mobility indicators.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
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