Inhalt

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   Einleitung und Vorwort
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   Abstract
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   Danksagung
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   Inhaltsangabe
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  1 Einführung
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   1.1 Motivation und Zielsetzung
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   1.2 Ansatz
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   1.3 Beitrag der Arbeit
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   1.4 Gliederung
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  2 Sensorik
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   2.1 CW-Modulationsinterferometrie
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   2.1.1 Entfernungsbestimmung mittels Homodynverfahren
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   2.2 MultiCam – Monokulares 2D/3D-Kamerasystem
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   2.2.1 Zeitliche Synchronisation
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   2.2.2 Laterale Registrierung
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   2.2.3 Vorverarbeitung (Präprozessierung) der Entfernungsdaten
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   2.2.3.1 Charakteristische Abweichfunktion
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   2.2.3.2 Radiale Korrektur
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   2.2.4 Simultane Operation mehrerer MultiCams
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   2.3 Fazit
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  3 Agentenparadigma-Systemtopologie
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  3.1 Das Agentenparadigma
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   3.1.1 Agentendefinition
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  3.1.2 BDI-Agentenmodell
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   3.1.2.1 Praktisches Schlussfolgern
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   Prozess der Erwägung (Deliberation)
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   Prozess der zielgerichteten Schlussfolgerung (Means-end reasoning)
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   3.1.2.2 Operationszyklus des BDI-Modells
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   3.1.3 PRS - Procedural Reasoning System
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   3.1.3.1 PRS-Operationszyklus
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  3.1.4 Agentenkommunikation
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   3.1.4.1 KQML
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   3.1.4.2 FIPA-ACL
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  3.1.5 Implementiertes Agentensystem
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   Weltmodell
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   Ziele
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   Pläne
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   Aktionen
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   Intentionsstruktur
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   Interpreter
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  3.1.6 Agentenarchitektur
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   Kontrollmodul
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   Kommunikationsmodul
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   Erweiterungsmodul
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  3.1.7 Konstitution der verwendeten Agenten
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   3.1.7.1 Multisensorknoten (MSK)
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   3.1.7.2 Fusionsknoten (FSK)
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   3.1.8 Zeitliche Synchronisation
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   3.2 Informationsfusion
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   3.2.1 Fusionstypen
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   3.2.2 Datenangleichung
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   3.2.3 JDL Datenfusionsprozess-Modell
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   Prozessebene 0:
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   Prozessebene 1:
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   Prozessebene 2:
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   Prozessebene 3:
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   Prozessebene 4:
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   Prozessebene 5:
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   3.2.4 Prozessablauf
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   3.3 Zusammenfassung
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   Anwendung des Agentenparadigmas im Projekt POmSe
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  4 Agentenbasierte Detektion, Lokalisation und Klassifikation von Objekten
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  4.1 Operationen auf JDL-Prozessebene 0
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   4.1.1 Verteilte Datenakquisition
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  4.1.2 Präprozessierung der Sensordaten
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   4.1.2.1 Distanzkorrektur
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   4.1.2.2 Radiale Korrektur
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   4.1.2.3 Entfernungsdatenfilterung, Plausibilitätstest
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   4.1.2.4 Modulationsdatenfilterung
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  4.2 Operationen auf JDL-Prozessebene 1
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   4.2.1 Objektdetektion
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   4.2.1.1 Deterministische Hintergrundmodellierung
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   4.2.1.2 Probabilistische Modellierung des Hintergrunds
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  4.2.2 Implementierter Algorithmus zur Objektdetektion
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   4.2.2.1 Bewegungsdetektion
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   4.2.2.2 Hintergrundmodellierung
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   4.2.2.3 Bestimmung der Vordergrundpixel
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  4.2.3 Objektlokalisation
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   4.2.3.1 Probabilistisches Clustering
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   Expectation-Maximization-Algorithmus (EM-Algorithmus)
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   Initialisierungsphase
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   1. Schritt: Berechnung der Erwartungswerte (Expectation)
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   2. Schritt: Maximierungsphase (Maximization)
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   4.2.3.2 Deterministisches Clustering k-Means
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  4.2.4 Implementierter Algorithmus zur Objektlokalisation
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   4.2.4.1 Erzeugung der Aktivitätskontur
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   4.2.4.2 Bildung der Aktivitätscluster
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   4.2.5 Zusätzliche Betrachtungen zur Objektlokalisation
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   4.3 Objektklassifikation
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   4.3.1 Objektklassifikation mittels AdaBoost
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   4.3.2 Objektklassifikation mittels HOG
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   4.3.3 Personenerkennung unter Verwendung kooperierender Fusion
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   4.3.3.1 Unterstützter AdaBoost-Algorithmus (A-AdaBoost)
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   4.3.3.2 Unterstützter HOG-Algorithmus (A-HOG)
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   4.4 Zusammenfassung
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   5 Objekt- und Personenverfolgung
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  5.1 Probabilistische Estimation
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   1. Schritt: Prädiktion
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   2. Schritt: Aktualisierung
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  5.2 Lokale Objekt- und Personenverfolgung (JDL-Prozessebene 1)
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   5.2.1 Lokales Objektmanagement
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   5.2.2 Kalman-Filter
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   Prädiktion
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   Aktualisierung
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   5.2.3 Estimation der Objektzustände
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   5.2.4 Analyse der Dynamik
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   5.2.5 Positionswichtung
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   5.2.6 Erzeugung des lokalen Zustandsvektors
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   5.2.7 Analyse des Zeitbedarfs zur Erzeugung lokaler Zustandsvektoren
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   5.3 Globale Objekt- und Personenverfolgung (JDL-Prozessebene 2)
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   5.3.1 Datenangleichung
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   5.3.2 Globales Objektmanagement
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  5.3.3 CONDENSATION-Algorithmus
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   5.3.3.1 Estimationsprozess
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  5.3.4 Estimation der Objektzustände
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   5.3.4.1 Filterinitialisierung
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   5.3.4.2 Objektverfolgung
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   5.3.5 Analyse der Filterdynamik
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  5.4 Steigerung des Informationsgehalts
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   5.4.1 Erweiterung des Detektionsbereichs
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  5.4.2 Reduktion gegenseitiger Objektverdeckungen
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   Wiederholte Verdeckungen durch unkoordinierte Bewegung
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   Partielle Verdeckungen durch ungünstige Perspektiven
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  5.5 Analyse des Zeitbedarfs zur Erzeugung globaler Informationen
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   5.5.1 Geschwindigkeit der Datenfusion
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   5.5.2 Geschwindigkeit des Gesamtsystems
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  6 Schlussbetrachtung
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   6.1 Potenzial der Sensorik
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  6.2 Betrachtung der Zielsetzung und Errungenschaften
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   Echtzeitfähige Operation
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   Einfache Skalierbarkeit
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   Operation unter fordernden Lichtverhältnissen
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   Funktionale Erweiterbarkeit
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   Zusätzliche Errungenschaften
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   6.3 Einsatzmöglichkeiten
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   6.4 Momentane Systembeschränkungen
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   6.5 Erweiterungsmöglichkeiten und Ausblick
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  A Lokales Objektmanagement
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   Verwendete Größen
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   Bestimmung zeitlich zusammenhängender Aktivitätscluster
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   Lokale Objekterzeugung
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   Objekterhaltung und Objektentfernung
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  B Globales Objektmanagement
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   Verwendete Größen
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   Annahmen
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   Berechnung der Distanzinformationen
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   Globale Objekterzeugung
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   Globale Erhaltung und Entfernung von Objekten
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  C Bayes'sche Estimation
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   Verwendete Größen
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   Annahmen
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   Estimationsprozess
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  D CONDENSATION-Algorithmus
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   Verwendete Größen
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   Annahmen
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   Estimationsprozess
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   1. Stufe: Erzeugung der Partikelmenge des Nachfolgezeitpunkts
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   2. Stufe: Anwendung der Systemdynamik (Prädiktion)
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   3. Stufe: Wichtung der Partikelmenge des Nachfolgezeitpunkts (Aktualisierung)
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  E Quaternionen
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   Rotationen durch Drehmatrizen
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   Rotationen durch Quaternionen
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   F Tabellen
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  G Literaturverzeichnis
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