Incremental Learning for Motion Prediction of Pedestrians and Vehicles (Record no. 112308)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03383nam a22004935i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-642-13642-9 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20140220084539.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 100715s2010 gw | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9783642136429 |
| -- | 978-3-642-13642-9 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1007/978-3-642-13642-9 |
| Source of number or code | doi |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | TJ210.2-211.495 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | T59.5 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | TJFM1 |
| Source | bicssc |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | TEC037000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | TEC004000 |
| Source | bisacsh |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.892 |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Govea, Alejandro Dizan Vasquez. |
| Relator term | author. |
| 245 10 - TITLE STATEMENT | |
| Title | Incremental Learning for Motion Prediction of Pedestrians and Vehicles |
| Medium | [electronic resource] / |
| Statement of responsibility, etc | by Alejandro Dizan Vasquez Govea. |
| 264 #1 - | |
| -- | Berlin, Heidelberg : |
| -- | Springer Berlin Heidelberg, |
| -- | 2010. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 160p. 35 illus. in color. |
| Other physical details | online resource. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 490 1# - SERIES STATEMENT | |
| Series statement | Springer Tracts in Advanced Robotics, |
| International Standard Serial Number | 1610-7438 ; |
| Volume number/sequential designation | 64 |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | I: Background -- Probabilistic Models -- II: State of the Art -- Intentional Motion Prediction -- Hidden Markov Models -- III: Proposed Approach -- Growing Hidden Markov Models -- Learning and Predicting Motion with GHMMs -- IV: Experiments -- Experimental Data -- Experimental Results -- V: Conclusion -- Conclusions and Future Work. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Modeling and predicting human and vehicle motion is an active research domain. Owing to the difficulty in modeling the various factors that determine motion (e.g. internal state, perception) this is often tackled by applying machine learning techniques to build a statistical model, using as input a collection of trajectories gathered through a sensor (e.g. camera, laser scanner), and then using that model to predict further motion. Unfortunately, most current techniques use offline learning algorithms, meaning that they are not able to learn new motion patterns once the learning stage has finished. This books presents a lifelong learning approach where motion patterns can be learned incrementally, and in parallel with prediction. The approach is based on a novel extension to hidden Markov models, and the main contribution presented in this book, called growing hidden Markov models, which gives us the ability to learn incrementally both the parameters and the structure of the model. The proposed approach has been extensively validated with synthetic and real trajectory data. In our experiments our approach consistently learned motion models that were more compact and accurate than those produced by two other state-of-the-art techniques, confirming the viability of lifelong learning approaches to build human behavior models. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Optical pattern recognition. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Robotics and Automation. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Artificial Intelligence (incl. Robotics). |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Pattern Recognition. |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY | |
| Title | Springer eBooks |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9783642136412 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | Springer Tracts in Advanced Robotics, |
| -- | 1610-7438 ; |
| Volume number/sequential designation | 64 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-3-642-13642-9 |
| 912 ## - | |
| -- | ZDB-2-ENG |
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