Incremental learning for motion prediction of pedestrians and vehicles:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Heidelberg
Springer
2010
|
| Series: | Springer tracts in advanced robotics
64 |
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Item Description: | Literaturangaben |
| Physical Description: | XVIII, 153 S. Ill., graph. Darst. 24 cm |
| ISBN: | 9783642136412 |
Staff View
MARC
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Record in the Search Index
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CONTENTS 1 INTRODUCTION 1 1.1 MOTIVATION 1 1.2 PROBLEM DESCRIPTION 2
1.2.1 MODELING MOTION WITH HIDDEN MARKOV MODELS 3 1.2.2 CHALLENGES 4 1.3
CONTRIBUTIONS 5 1.4 OVERVIEW OF THE REST OF THIS BOOK 6 PART I:
BACKGROUND 2 PROBABILISTIC MODELS 11 2.1 OVERVIEW 11 2.2 FROM LOGIC TO
PROBABILITIES 12 2.2.1 LOGIC PROPOSITIONS 12 2.2.2 PROBABILITY OF A
PROPOSITION 12 2.2.3 VARIABLES 13 2.2.4 JPD DECOMPOSITION AND
CONDITIONAL INDEPENDENCE 16 2.2.5 INFERENCE 17 2.2.6 PARAMETRIC FORMS 18
2.2.7 LEARNING 19 2.3 THE BAYES FILTER 21 2.3.1 PROBABILISTIC MODEL 21
2.3.2 PARAMETRIC FORMS 22 2.3.3 INFERENCE 23 2.3.4 SPECIALIZATIONS OF
THE BAYES FILTER 23 2.4 DISCUSSION 24 BIBLIOGRAFISCHE INFORMATIONEN
HTTP://D-NB.INFO/1002540135 DIGITALISIERT DURCH 4.7 DISCUSSION 67 XIV
CONTENTS PART II: STATE OF THE ART 3 INTENTIONAL MOTION PREDICTION 27
3.1 OVERVIEW 27 3.2 A NOTE ON SEMANTICS 30 3.3 TRAJECTORY PROTOTYPES 30
3.3.1 REPRESENTATION 30 3.3.2 LEARNING 31 3.3.3 PREDICTION 33 3.4
DISCRETE STATE-SPACE MODELS 34 3.4.1 REPRESENTATION 35 3.4.2 LEARNING 35
3.4.3 PREDICTION 36 3.4.4 OTHER STATE-SPACE MODELS 36 3.5 OTHER
APPROACHES 38 3.5.1 NEURAL NETWORK BASED APPROACHES 38 3.5.2 GOAL
ORIENTED APPROACHES 38 3.5.3 OTHER FIELDS 39 3.6 DISCUSSION 39 3.6.1
GENERAL ISSUES 40 3.6.2 STATE-SPACE MODEL ISSUES 41 4 HIDDEN MARKOV
MODELS 45 4.1 OVERVIEW 45 4.2 PROBABILISTIC MODEL 45 4.2.1 VARIABLES 46
4.2.2 DECOMPOSITION 46 4.2.3 PARAMETRIC FORMS 46 4.2.4 EXAMPLE: THE
BROKEN AIR CONDITIONING SYSTEM 47 4.3 INFERENCE 48 4.3.1 ON-LINE
INFERENCE 48 4.3.2 OFF-LINE INFERENCE 52 4.3.3 NUMERICAL STABILITY AND
HMM SCALING 57 4.4 PARAMETER LEARNING 58 4.4.1 THE BAUM-WELCH ALGORITHM
58 4.4.2 INCREMENTAL ALGORITHMS 60 4.5 TRANSITION STRUCTURE 61 4.6
STRUCTURE LEARNING 63 4.6.1 LOCAL SEARCH ALGORITHMS 64 4.6.2 STATE
MERGING ALGORITHMS 66 4.6.3 OTHER ALGORITHMS 67 7.5 DISCUSSION 101
CONTENTS XV PART III: PROPOSED APPROACH 5 GROWING HIDDEN MARKOV MODELS
71 5.1 OVERVIEW 71 5.2 PROBABILISTIC MODEL 72 5.2.1 VARIABLES 72 5.2.2
DECOMPOSITION 73 5.2.3 PARAMETRIC FORMS 73 5.3 INFERENCE 73 5.4 LEARNING
74 5.4.1 TOPOLOGICAL MAP 74 5.4.2 UPDATING THE TOPOLOGICAL MAP 76 5.4.3
UPDATING THE MODEL'S STRUCTURE 77 5.4.4 UPDATING THE PARAMETERS 78 5.4.5
PROPERTIES 79 5.4.6 LEARNING THE COVARIANCE 81 5.5 DISCUSSION 81 6
LEARNING AND PREDICTING MOTION WITH GHMMS 83 6.1 OVERVIEW 83 6.2
NOTATION AND BASIC ASSUMPTIONS 84 6.3 PROBABILISTIC MODEL 85 6.3.1
VARIABLES 85 6.3.2 DECOMPOSITION 85 6.3.3 PARAMETRIC FORMS 86 6.4
PREDICTION 87 6.5 STRUCTURE AND PARAMETER LEARNING 88 6.6 LEARNING
EXAMPLE: A UNIDIMENSIONAL ENVIRONMENT 88 6.6.1 DEFINING THE STATE 88
6.6.2 CHOOSING THE PARAMETERS OF THE LEARNING ALGORITHM 89 6.6.3 LEARNED
MODEL 89 6.7 COMPARISON WITH EXISTING HMM BASED APPROACHES 91 6.8
DISCUSSION 94 PART IV: EXPERIMENTS 7 EXPERIMENTAL DATA 97 7.1 OVERVIEW
97 7.2 REAL DATA: LEEDS DATA SET 97 7.3 SYNTHETIC DATA: PARKING
SIMULATOR 98 7.4 THE INRIA ENTRY HALL 99 147 XVI CONTENTS 8 EXPERIMENTAL
RESULTS 103 8.1 OVERVIEW 103 8.2 QUALITATIVE RESULTS 104 8.2.1 LEEDS
DATA 104 8.2.2 SIMULATED DATA 108 8.3 QUANTITATIVE RESULTS 109 8.3.1
PARAMETER SELECTION 109 8.3.2 MEASURING PREDICTION ACCURACY 110 8.3.3
LEEDS PARKING DATA 110 8.3.4 SYNTHETIC PARKING DATA 113 8.4 MODELING
CYCLIC MOTION 114 8.5 COMPARISON WITH OTHER APPROACHES 117 8.5.1
COMPARED APPROACHES 117 8.5.2 COMPARISON CRITERIA 119 8.5.3 LEEDS DATA
120 8.5.4 SYNTHETIC PARKING DATA 122 8.5.5 EVALUATION CONCLUSION 125 8.6
DISCUSSION 126 PART V: CONCLUSION 9 CONCLUSIONS AND FUTURE WORK 131 9.1
CONCLUSIONS 131 9.2 FUTURE WORK AND POSSIBLE EXTENSIONS 133 9.2.1
HIGH-LEVEL EXTENSIONS 133 9.2.2 LOW-LEVEL EXTENSIONS 135 A VECTOR
QUANTIZATION AND TOPOLOGY REPRESENTING NETWORKS 137 A.0.1 TOPOLOGY
REPRESENTING NETWORKS 139 B COMPUTING FUZZY K-MEANS CLUSTER
REPRESENTATIONS 143 C NOTATION AND ABBREVIATIONS 145 REFERENCES |
| any_adam_object | 1 |
| author | Vasquez Govea, Alejandro Dizan |
| author_facet | Vasquez Govea, Alejandro Dizan |
| author_role | aut |
| author_sort | Vasquez Govea, Alejandro Dizan |
| author_variant | g a d v gad gadv |
| building | Verbundindex |
| bvnumber | BV036638022 |
| classification_rvk | ZO 4650 |
| classification_tum | DAT 708f DAT 815f |
| ctrlnum | (OCoLC)658235334 (DE-599)DNB1002540135 |
| dewey-full | 629.276 629.892631 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 629 - Other branches of engineering |
| dewey-raw | 629.276 629.892631 |
| dewey-search | 629.276 629.892631 |
| dewey-sort | 3629.276 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Maschinenbau / Maschinenwesen Informatik Mathematik Verkehr / Transport Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| format | Book |
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| indexdate | 2024-07-20T10:45:09Z |
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| isbn | 9783642136412 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020557707 |
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| publisher | Springer |
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| series | Springer tracts in advanced robotics |
| series2 | Springer tracts in advanced robotics |
| spelling | Vasquez Govea, Alejandro Dizan Verfasser aut Incremental learning for motion prediction of pedestrians and vehicles Alejandro Dizan Vasquez Govea Berlin ; Heidelberg Springer 2010 XVIII, 153 S. Ill., graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Springer tracts in advanced robotics 64 Literaturangaben Inkrementelles Lernen (DE-588)4419135-2 gnd rswk-swf Kraftfahrzeug (DE-588)4073757-3 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Parken (DE-588)4044677-3 gnd rswk-swf Bewegungsanalyse Technik (DE-588)4302721-0 gnd rswk-swf Fahrerassistenzsystem (DE-588)4622983-8 gnd rswk-swf Hidden-Markov-Modell (DE-588)4352479-5 gnd rswk-swf Robotik (DE-588)4261462-4 gnd rswk-swf Bahnplanung (DE-588)4267628-9 gnd rswk-swf Robotik (DE-588)4261462-4 s Bahnplanung (DE-588)4267628-9 s Bewegungsanalyse Technik (DE-588)4302721-0 s Maschinelles Lernen (DE-588)4193754-5 s Inkrementelles Lernen (DE-588)4419135-2 s Hidden-Markov-Modell (DE-588)4352479-5 s DE-604 Kraftfahrzeug (DE-588)4073757-3 s Parken (DE-588)4044677-3 s Fahrerassistenzsystem (DE-588)4622983-8 s Springer tracts in advanced robotics 64 (DE-604)BV016421724 64 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3476772&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=020557707&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Vasquez Govea, Alejandro Dizan Incremental learning for motion prediction of pedestrians and vehicles Springer tracts in advanced robotics Inkrementelles Lernen (DE-588)4419135-2 gnd Kraftfahrzeug (DE-588)4073757-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Parken (DE-588)4044677-3 gnd Bewegungsanalyse Technik (DE-588)4302721-0 gnd Fahrerassistenzsystem (DE-588)4622983-8 gnd Hidden-Markov-Modell (DE-588)4352479-5 gnd Robotik (DE-588)4261462-4 gnd Bahnplanung (DE-588)4267628-9 gnd |
| subject_GND | (DE-588)4419135-2 (DE-588)4073757-3 (DE-588)4193754-5 (DE-588)4044677-3 (DE-588)4302721-0 (DE-588)4622983-8 (DE-588)4352479-5 (DE-588)4261462-4 (DE-588)4267628-9 |
| title | Incremental learning for motion prediction of pedestrians and vehicles |
| title_auth | Incremental learning for motion prediction of pedestrians and vehicles |
| title_exact_search | Incremental learning for motion prediction of pedestrians and vehicles |
| title_full | Incremental learning for motion prediction of pedestrians and vehicles Alejandro Dizan Vasquez Govea |
| title_fullStr | Incremental learning for motion prediction of pedestrians and vehicles Alejandro Dizan Vasquez Govea |
| title_full_unstemmed | Incremental learning for motion prediction of pedestrians and vehicles Alejandro Dizan Vasquez Govea |
| title_short | Incremental learning for motion prediction of pedestrians and vehicles |
| title_sort | incremental learning for motion prediction of pedestrians and vehicles |
| topic | Inkrementelles Lernen (DE-588)4419135-2 gnd Kraftfahrzeug (DE-588)4073757-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Parken (DE-588)4044677-3 gnd Bewegungsanalyse Technik (DE-588)4302721-0 gnd Fahrerassistenzsystem (DE-588)4622983-8 gnd Hidden-Markov-Modell (DE-588)4352479-5 gnd Robotik (DE-588)4261462-4 gnd Bahnplanung (DE-588)4267628-9 gnd |
| topic_facet | Inkrementelles Lernen Kraftfahrzeug Maschinelles Lernen Parken Bewegungsanalyse Technik Fahrerassistenzsystem Hidden-Markov-Modell Robotik Bahnplanung |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=3476772&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020557707&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV016421724 |
| work_keys_str_mv | AT vasquezgoveaalejandrodizan incrementallearningformotionpredictionofpedestriansandvehicles |