Monoscopic object-recognition for advanced driver assistance systems:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
München
Verl. Dr. Hut
2011
|
| Ausgabe: | 1. Aufl. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIII, 203 S. Ill., graph. Darst. |
| ISBN: | 9783843901888 |
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| 245 | 1 | 0 | |a Monoscopic object-recognition for advanced driver assistance systems |c Andreas Laika |
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Datensatz im Suchindex
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| adam_text | IMAGE 1
CONTENTS
ABSTRACT III
KURZFASSUNG V
ACKNOWLEDGMENTS VII
1 INTRODUCTION 1
1.1 MOTIVATION 1
1.2 GOAL OF THE THESIS 2
1.3 RESULT PREVIEW 3
1.4 CURRENT DRIVER ASSISTANCE SYSTEMS 3
1.5 OUTLINE OF THE THESIS 5
2 BASIC PRINCIPLES 7
2.1 IMAGE FORMATION 7
2.1.1 CAMERA GEOMETRY 8
2.1.2 TWO VIEW GEOMETRY 10
2.2 OPTICAL FLOW 12
2.2.1 DEFINITION AND NOTATION OF OPTICAL ROW 12
2.2.2 METHODS FOR COMPUTING OPTICAL FLOW 12
2.2.3 COMPUTING OPTICAL FLOW USING THE PYRAMIDAL IMPLEMENTATION OF THE
LUCAS KANADE FEATURE TRACKER 14
2.3 VISUAL OBJECT RECOGNITION: DETECTION VERSUS CLASSIFICATION 15 2.4
VISUAL OBJECT DETECTION 16
2.5 VISUAL OBJECT CLASSIFICATION 17
2.5.1 OVERVIEW ON VISUAL OBJECT CLASSIFICATION 17
2.5.2 FEATURE EXTRACTION 18
2.5.2.1 MPEG7 VISUAL DESCRIPTORS 19
2.5.2.2 HISTOGRAMS OF ORIENTED GRADIENTS 22
2.5.3 CLASSIFICATION USING MACHINE LEARNING 24
2.5.3.1 THE SUPPORT VECTOR MACHINE CLASSIFIER 24
IX
HTTP://D-NB.INFO/1017746818
IMAGE 2
3 OBJECT-RECOGNITION IN T H E CONTEXT OF AUTOMOTIVE SYSTEMS 2 7
3.1 EXPERIMENTAL VEHICLE AND SENSORS 27
3.2 THE PROCESSING SYSTEM 28
3.2.1 PROCESSING FRAMEWORK 28
3.2.2 PROCESSING CHAIN 29
4 COMPUTING OPTICAL FLOW 31
4.1 INTRODUCTION 31
4.2 STATE OF THE ART 32
4.2.1 MATCHING TECHNIQUES USING THE CENSUS TRANSFORMATION 32 4.2.2
COMPUTING OPTICAL FLOW USING THE EXTENDED CENSUS TRANSFOR MATION 32
4.3 METRICS FOR EVALUATING OPTICAL FLOW 35
4.4 PARAMETERS 37
4.4.1 INFLUENCE OF THE SIMILARITY PARAMETER EPSILON 37
4.4.2 INFLUENCE OF SAMPLING DISTANCE 38
4.4.3 INFLUENCE OF MDP 39
4.4.4 INFLUENCE OF CARDINALITY 40
4.4.5 INFLUENCE OF SAMPLING CONFIGURATION 41
4.5 IMPLEMENTATION IN HARDWARE AND SOFTWARE 43
4.6 AN ALGORITHM OPTIMIZED FOR A SOFTWARE IMPLEMENTATION 44
4.7 AN ALGORITHM OPTIMIZED FOR A HARDWARE IMPLEMENTATION 46
4.7.1 MODIFICATIONS NECESSARY FOR A HARDWARE IMPLEMENTATION . . . . 46
4.7.2 STRUCTURE AND IMPLEMENTATION OF THE ALGORITHM 48
4.8 CHARACTERISTICS OF HW- AND SW-IMPLEMENTATION 53
4.8.1 PROPERTIES OF THE SW-VERSION 53
4.8.2 PROPERTIES OF THE HW-VERSION 54
4.9 EVALUATION 57
4.9.1 DETAILED COMPARISON ON DENSITY AND ACCURACY 57
4.9.1.1 EVALUATION OF THE SW-VERSION 58
4.9.1.2 EVALUATION OF HW-VERSION 60
4.9.2 DETAILED COMPARISON ON THE EXECUTION TIME 63
4.9.3 JOINT COMPARISON ON QUALITY AND EXECUTION TIME 64
4.10 SUMMARY 66
4.10.1 CONCLUSIONS 66
4.10.2 OUTLOOK 67
X
IMAGE 3
5 HYPOTHESIS GENERATION - DETECTING A N D LOCALIZING OBJECTS 69
5.1 INTRODUCTION 69
5.2 MOTION-BASED OBJECT DETECTION 70
5.2.1 STATE OF THE ART 70
5.2.2 AN ALGORITHM UTILIZING MOTION FOR THE GENERATION OF OBJECT HY
POTHESES 75
5.2.2.1 STRUCTURE OF THE ALGORITHM 75
5.2.2.2 COMPENSATING FOR EGOMOTION 77
5.2.2.3 THE CLUSTERING METHOD 78
5.2.2.4 IMPLEMENTATION OF THE ALGORITHM FOR THE GENERATION OF OBJECT
HYPOTHESES 79
5.2.3 EVALUATION METHOD 81
5.2.3.1 MEASURING DETECTION 82
5.2.3.2 MEASURING FRAGMENTATION 84
5.2.3.3 MEASURING LOCALIZATION 84
5.2.4 RESULTS 85
5.2.4.1 EVALUATION WITH RESPECT TO ACCURACY 86
5.2.4.2 EVALUATION WITH RESPECT TO RUNTIME 92
5.2.4.3 COMPARISON WITH OTHER APPROACHES 93
5.3 LOKALIZING OBJECTS BY CONTOUR EXTRACTION 96
5.3.1 OBJECTIVE 96
5.3.2 STATE OF THE ART 96
5.3.3 AN ALGORITHM GROUPING EDGES FOR CONTOUR EXTRACTION 98
5.3.3.1 PREPROCESSING 98
5.3.3.2 PROMINENCE-MAP GENERATION 98
5.3.3.3 GENERATING A GRAPH REPRESENTATION 100
5.3.3.4 GRAPH-BASED SEARCH 102
5.3.3.5 POSTPROCESSING 104
5.3.3.6 SUMMARY OFT THE PARAMETERS 105
5.3.4 EVALUATION 106
5.3.5 RESULTS 107
5.3.5.1 INFLUENCE OF THE ALGORITHM S PARAMETERS 107
5.3.5.2 COMPARISON WITH THE ORIGINAL FKG METHOD I L L
5.3.5.3 COMPARISON WITH GVF SNAKES 112
5.3.5.4 REAL-TIME PERFORMANCE 114
5.4 SUMMARY 115
5.4.1 CONCLUSIONS AND OUTLOOK REGARDING MOTION-BASED OBJECT DE TECTION
115
5.4.2 CONCLUSIONS AND OUTLOOK REGARDING CONTOUR EXTRACTION . . . . 116
XI
IMAGE 4
HYPOTHESIS VERIFICATION - VISUAL OBJECT CLASSIFICATION 119
6.1 PROBLEM STATEMENT 119
6.2 STATE OF THE ART 120
6.2.1 FEATURE EXTRACTION-METHODS FOR VISUAL OBJECT CLASSIFICATION . . .
122 6.2.1.1 FEATURES USING CONTOUR 123
6.2.1.2 FEATURES USING PATTERNS OF CONTRAST AND IMAGE GRADIENTS 124
6.2.1.3 FEATURES USING MOTION 125
6.2.2 THE INFLUENCE OF DISTORTIONS 126
6.3 TEST SETUP AND METHOD OF EVALUATION 127
6.3.1 TEST SYSTEM OVERVIEW 127
6.3.1.1 DATASET AND REPOSITORY 128
6.3.1.2 AGGREGATION OF DATASETS FOR DIFFERENT TEST-CASES . . . . 128
6.3.1.3 SIMULATING DEFECTIVE IMAGE-ACQUISITION 130
6.3.1.4 FEATURE EXTRACTION AND MACHINE LEARNING 130
6.3.1.5 EVALUATION 131
6.3.2 EVALUATION METRICS 132
6.3.2.1 EVALUATION-METHODS FOR BINARY CLASSIFICATION 132 6.3.2.2
CROSS-VALIDATION FOR BINARY CLASSIFICATION 137
6.3.2.3 EVALUATION-METHODS FOR MULTICLASS 138
6.3.2.4 MULTICLASS CROSS-VALIDATION 140
6.3.2.5 TESTING FOR STATISTICAL SIGNIFICANCE 141
6.4 VISUAL OBJECT CLASSIFICATION CONSIDERING DIFFERENT KINDS OF OBJECT
CLASSES 142 6.4.1 IMPLEMENTATION 142
6.4.2 RESULTS 143
6.5 VISUAL OBJECT CLASSIFICATION CONSIDERING DISTORTED IMAGE-ACQUISITION
. . 147 6.5.1 IMPLEMENTATION 147
6.5.2 RESULTS 149
6.5.2.1 ACQUISITION - LOCATION 150
6.5.2.2 ACQUISITION - IMAGE 152
6.5.2.3 COMPARISON WITH OTHER APPROACHES 157
6.6 VISUAL OBJECT CLASSIFICATION USING DIFFERENT TYPES OF FEATURES 158
6.6.1 AN ALGORITHM EXTRACTING FEATURES FROM SPARSE OPTICAL FLOW . . .
158 6.6.2 IMPLEMENTATION 161
6.6.3 RESULTS 163
6.6.3.1 IN DEPTH EVALUATION OF MOTION-FEATURES 163
6.6.3.2 GENERAL EVALUATION OF FEATURES 167
6.6.3.3 COMPARISON WITH OTHER APPROACHES 168
6.7 SUMMARY 169
6.7.1 CONCLUSIONS 169
6.7.2 OUTLOOK 171
IMAGE 5
7 CONCLUSIONS 173
7.1 SCIENTIFIC CONTRIBUTIONS 173
7.2 SUGGESTIONS ON FUTURE WORK 175
A DESCRIPTION OF THE DATASETS U S E D FOR EVALUATION 177
A.L A TOOL FOR LABELING DATA 177
A.2 OBJECT DETECTION DATASET 178
A.3 CLASSIFICATION DATASET 180
A.4 OPTICAL FLOW DATASET 180
LIST OF FIGURES 183
LIST OF TABLES 185
OWN PUBLICATIONS AND SUPERVISED WORK 187
BIBLIOGRAPHY 203
XIII
|
| any_adam_object | 1 |
| author | Laika, Andreas |
| author_facet | Laika, Andreas |
| author_role | aut |
| author_sort | Laika, Andreas |
| author_variant | a l al |
| building | Verbundindex |
| bvnumber | BV039882313 |
| ctrlnum | (OCoLC)780115559 (DE-599)BVBBV039882313 |
| dewey-full | 629.276 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 629 - Other branches of engineering |
| dewey-raw | 629.276 |
| dewey-search | 629.276 |
| dewey-sort | 3629.276 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Verkehr / Transport |
| edition | 1. Aufl. |
| format | Thesis Book |
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| spelling | Laika, Andreas Verfasser aut Monoscopic object-recognition for advanced driver assistance systems Andreas Laika 1. Aufl. München Verl. Dr. Hut 2011 XIII, 203 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Zugl.: München, Techn. Univ., Diss., 2011 Objekterkennung (DE-588)4314334-9 gnd rswk-swf Echtzeitbildverarbeitung (DE-588)4150999-7 gnd rswk-swf Vektorfeld (DE-588)4139571-2 gnd rswk-swf Bewegungsanalyse Technik (DE-588)4302721-0 gnd rswk-swf Fahrerassistenzsystem (DE-588)4622983-8 gnd rswk-swf Automatische Klassifikation (DE-588)4120957-6 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Fahrerassistenzsystem (DE-588)4622983-8 s Objekterkennung (DE-588)4314334-9 s Bewegungsanalyse Technik (DE-588)4302721-0 s Echtzeitbildverarbeitung (DE-588)4150999-7 s Vektorfeld (DE-588)4139571-2 s Automatische Klassifikation (DE-588)4120957-6 s DE-604 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024741514&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Laika, Andreas Monoscopic object-recognition for advanced driver assistance systems Objekterkennung (DE-588)4314334-9 gnd Echtzeitbildverarbeitung (DE-588)4150999-7 gnd Vektorfeld (DE-588)4139571-2 gnd Bewegungsanalyse Technik (DE-588)4302721-0 gnd Fahrerassistenzsystem (DE-588)4622983-8 gnd Automatische Klassifikation (DE-588)4120957-6 gnd |
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| title | Monoscopic object-recognition for advanced driver assistance systems |
| title_auth | Monoscopic object-recognition for advanced driver assistance systems |
| title_exact_search | Monoscopic object-recognition for advanced driver assistance systems |
| title_full | Monoscopic object-recognition for advanced driver assistance systems Andreas Laika |
| title_fullStr | Monoscopic object-recognition for advanced driver assistance systems Andreas Laika |
| title_full_unstemmed | Monoscopic object-recognition for advanced driver assistance systems Andreas Laika |
| title_short | Monoscopic object-recognition for advanced driver assistance systems |
| title_sort | monoscopic object recognition for advanced driver assistance systems |
| topic | Objekterkennung (DE-588)4314334-9 gnd Echtzeitbildverarbeitung (DE-588)4150999-7 gnd Vektorfeld (DE-588)4139571-2 gnd Bewegungsanalyse Technik (DE-588)4302721-0 gnd Fahrerassistenzsystem (DE-588)4622983-8 gnd Automatische Klassifikation (DE-588)4120957-6 gnd |
| topic_facet | Objekterkennung Echtzeitbildverarbeitung Vektorfeld Bewegungsanalyse Technik Fahrerassistenzsystem Automatische Klassifikation Hochschulschrift |
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