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Incremental Learning for Motion Prediction of Pedestrians and Vehicles (Record no. 11268)

000 -LEADER
fixed length control field 03132nam a22004215i 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140310143344.0
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
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 629.892
Edition number 23
264 #1 -
-- Berlin, Heidelberg :
-- Springer Berlin Heidelberg,
-- 2010.
912 ## -
-- ZDB-2-ENG
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Govea, Alejandro Dizan Vasquez.
Relator term author.
245 10 - IMMEDIATE SOURCE OF ACQUISITION NOTE
Title Incremental Learning for Motion Prediction of Pedestrians and Vehicles
Medium [electronic resource] /
Statement of responsibility, etc by Alejandro Dizan Vasquez Govea.
300 ## - PHYSICAL DESCRIPTION
Extent 160p. 35 illus. in color.
Other physical details online resource.
440 1# - SERIES STATEMENT/ADDED ENTRY--TITLE
Title 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.
Topical term or geographic name as entry element Artificial intelligence.
Topical term or geographic name as entry element Optical pattern recognition.
Topical term or geographic name as entry element Engineering.
Topical term or geographic name as entry element Robotics and Automation.
Topical term or geographic name as entry element Artificial Intelligence (incl. Robotics).
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
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-642-13642-9
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Item type E-Book
Copies
Price effective from Permanent location Date last seen Not for loan Date acquired Source of classification or shelving scheme Koha item type Damaged status Lost status Withdrawn status Current location Full call number
2014-03-31AUM Main Library2014-03-31 2014-03-31 E-Book   AUM Main Library629.892

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