Machine learning in advanced driver-assistance systems: Contributions to pedestrian detection and adversarial modeling
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis Book |
| Language: | English |
| Published: |
Berlin
Logos Berlin
2019
|
| Series: | Studien zur Mustererkennung
Band 47 |
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Physical Description: | 137 Seiten 21 cm x 14.5 cm |
| ISBN: | 9783832548742 3832548742 |
Staff View
MARC
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| 245 | 1 | 0 | |a Machine learning in advanced driver-assistance systems |b Contributions to pedestrian detection and adversarial modeling |c Farzin Ghorban Rajabizadeh |
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| 653 | |a Machine Learning | ||
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Record in the Search Index
| _version_ | 1804180002964504576 |
|---|---|
| adam_text | CONTENTS
1
INTRODUCTION
1
2
MACHINE
LEARNING
FOR
OBJECT
DETECTION
5
2.1
INTRODUCTION
................................................................................
5
2.2
MACHINE
LEARNING
.......................................................................
7
2.2.1
ENSEMBLE
LEARNING
.......................................................
8
2.2.2
CONNECTIONISM
..............................................................
16
2.3
PROPOSAL
GENERATION
....................................................................
26
2.4
FEATURE
REPRESENTATION
.............................................................
28
2.5
CLUSTERING
THE
DETECTIONS
..........................................................
34
3
DATASETS
AND
METHODOLOGY
39
3.1
INTRODUCTION
................................................................................
39
3.2
PEDESTRIAN
DETECTION
................................................................
39
3.2.1
CALTECH
BENCHMARK
.......................................................
42
3.2.2
KITTI
BENCHMARK
..........................................................
44
3.3
TRAFFIC
SIGN
SAMPLES
....................................................................
45
4
AGGREGATED
CHANNELS
NETWORK
A
NOVEL
APPROACH
FOR
DETECTING
PEDESTRIANS
IN
REAL
TIME
47
4.1
MOTIVATION
..................................................................................
47
4.1.1
RELATED
WORK
.................................................................
48
4.1.2
CONTRIBUTION
.................................................................
49
IV
___________________________________________________
CONTENTS
4.2
AGGREGATED
CHANNEL
NETWORK
.....................................................
50
4.2.1
PROPOSAL
GENERATION
........................................................
50
4.2.2
PROPOSAL
EVALUATION
........................................................
51
4.3
IMPLEMENTATION
DETAILS
..............................................................
54
4.3.1
DATASET
..........................................................................
54
4.3.2
OPERATIONAL
POINT
...........................................................
54
4.3.3
COLLECTING
TRAINING
SET
..................................................
55
4.3.4
TRAINING
ACNET
..............................................................
57
4.3.5
EXTENDING
THE
PIPELINE
..................................................
58
4.3.6
IMPROVING
THE
QUALITY
OF
THE
PROPOSALS
......................
61
4.4
EXPERIMENTS
.................................................................................
64
4.4.1
COMPARISON
TO
THE
STATE-OF-THE-ART
METHODS
............
66
4.4.2
RUNTIME
ANALYSIS
...........................................................
69
4.5
EPILOGUE
.......................................................................................
71
5
INSATIATE
CASCADED
BOOSTED
FOREST
A
NOVEL
APPROACH
FOR
EXPLOITING
THE
ASYMMETRY
OF
THE
DATASET
73
5.1
MOTIVATION
....................................................................................
73
5.1.1
RELATED
WORK
.................................................................
75
5.1.2
CONTRIBUTION
.................................................................
77
5.2
ENSEMBLE
....................................................................................
77
5.2.1
DATASET
..........................................................................
77
5.2.2
SAMPLING
.......................................................................
78
5.2.3
MODEL
SETTINGS
.................................................................
79
5.3
EXPLOITING
THE
ASYMETRY
............................................................
80
5.3.1
PRELIMINARY
EXPERIMENT
..............................................
80
5.3.2
GREEDILY
BOOTSTRAPPING
..................................................
82
5.3.3
POSITIVE
MINING
..............................................................
83
CONTENTS
V
5.3.4
MODEL
PARAMETERS
............................................................
85
5.4
EXPERIMENTS
................................................................................
87
5.4.1
COMPARISON
TO
THE
STATE-OF-THE-ART
METHODS
..............
89
5.5
EPILOGUE
......................................................................................
92
6
CONDITIONAL
MULTICHANNEL
GENERATIVE
ADVERSARIAL
NETWORKS
A
NOVEL
APPROACH
FOR
GENERATING
SYNTHETIC
TRAFFIC
SIGNS
93
6.1
MOTIVATION
...................................................................................
93
6.1.1
RELATED
WORK
.................................................................
94
6.1.2
CONTRIBUTION
.................................................................
96
6.2
EVOLVING
THE
MODELING
FRAMEWORK
...........................................
97
6.2.1
INPUT
SIGNAL
....................................................................
97
6.2.2
GENERATIVE
ADVERSARIAL
NETWORKS
..................................
98
6.2.3
ARCHITECTURAL
DESIGN
....................................................
99
6.2.4
MULTICHANNEL
GANS
...........................................................
102
6.2.5
CONDITIONAL
MCGANS
.....................................................
103
6.3
EXPERIMENTS
...................................................................................
106
6.3.1
A
CLOSER
EXAMINATION
OF
SYNTHETIC
TRAFFIC
SIGNS
....
106
6.3.2
QUALITY
OF
THE
GENERATED
SAMPLES
..................................
109
6.3.3
PIXEL
TO
SUBPIXEL
GENERATOR
..............................................
110
6.4
EPILOGUE
..........................................................................................
112
7
CONCLUSION
AND
DISCUSSION
113
BIBLIOGRAPHY
119
|
| any_adam_object | 1 |
| author | Ghorban, Farzin |
| author_GND | (DE-588)1184632812 |
| author_facet | Ghorban, Farzin |
| author_role | aut |
| author_sort | Ghorban, Farzin |
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| building | Verbundindex |
| bvnumber | BV045863908 |
| classification_rvk | ZO 4260 |
| ctrlnum | (OCoLC)1101132502 (DE-599)DNB1181888921 |
| discipline | Informatik Verkehr / Transport |
| format | Thesis Book |
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| spelling | Ghorban, Farzin Verfasser (DE-588)1184632812 aut Machine learning in advanced driver-assistance systems Contributions to pedestrian detection and adversarial modeling Farzin Ghorban Rajabizadeh Berlin Logos Berlin 2019 137 Seiten 21 cm x 14.5 cm txt rdacontent n rdamedia nc rdacarrier Studien zur Mustererkennung Band 47 Dissertation Bergische Universität Wuppertal Fahrerassistenzsystem (DE-588)4622983-8 gnd rswk-swf Fußgänger (DE-588)4140907-3 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Objekterkennung (DE-588)4314334-9 gnd rswk-swf Deep Learning Neural Networks Machine Learning Objekterkennung Representation Learning Bergische Universität Wuppertal (DE-588)4113937-9 Hochschulschrift gnd-content Fahrerassistenzsystem (DE-588)4622983-8 s Objekterkennung (DE-588)4314334-9 s Fußgänger (DE-588)4140907-3 s DE-604 Maschinelles Lernen (DE-588)4193754-5 s Logos Verlag Berlin (DE-588)1065538812 pbl Studien zur Mustererkennung Band 47 (DE-604)BV013645858 47 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=ed0560b83cbe46128b53d7f573a975c8&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031247346&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ghorban, Farzin Machine learning in advanced driver-assistance systems Contributions to pedestrian detection and adversarial modeling Studien zur Mustererkennung Fahrerassistenzsystem (DE-588)4622983-8 gnd Fußgänger (DE-588)4140907-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Objekterkennung (DE-588)4314334-9 gnd |
| subject_GND | (DE-588)4622983-8 (DE-588)4140907-3 (DE-588)4193754-5 (DE-588)4314334-9 (DE-588)4113937-9 |
| title | Machine learning in advanced driver-assistance systems Contributions to pedestrian detection and adversarial modeling |
| title_auth | Machine learning in advanced driver-assistance systems Contributions to pedestrian detection and adversarial modeling |
| title_exact_search | Machine learning in advanced driver-assistance systems Contributions to pedestrian detection and adversarial modeling |
| title_full | Machine learning in advanced driver-assistance systems Contributions to pedestrian detection and adversarial modeling Farzin Ghorban Rajabizadeh |
| title_fullStr | Machine learning in advanced driver-assistance systems Contributions to pedestrian detection and adversarial modeling Farzin Ghorban Rajabizadeh |
| title_full_unstemmed | Machine learning in advanced driver-assistance systems Contributions to pedestrian detection and adversarial modeling Farzin Ghorban Rajabizadeh |
| title_short | Machine learning in advanced driver-assistance systems |
| title_sort | machine learning in advanced driver assistance systems contributions to pedestrian detection and adversarial modeling |
| title_sub | Contributions to pedestrian detection and adversarial modeling |
| topic | Fahrerassistenzsystem (DE-588)4622983-8 gnd Fußgänger (DE-588)4140907-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Objekterkennung (DE-588)4314334-9 gnd |
| topic_facet | Fahrerassistenzsystem Fußgänger Maschinelles Lernen Objekterkennung Hochschulschrift |
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| volume_link | (DE-604)BV013645858 |
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