Overview
- Recent research in the area of motion prediction of Pedestrians and Vehicles
- Presents the modeling, learning and prediction of motion
- Based on the winning thesis of the EURON Georges Giralt award
Part of the book series: Springer Tracts in Advanced Robotics (STAR, volume 64)
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Table of contents (9 chapters)
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Front Matter
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Introduction
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Part I: Background
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Front Matter
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Part II: State of the Art
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Front Matter
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Part III: Proposed Approach
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Front Matter
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Part IV: Experiments
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Front Matter
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Part V: Conclusion
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Front Matter
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Back Matter
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Incremental Learning for Motion Prediction of Pedestrians and Vehicles
Authors: Alejandro Dizan Vasquez Govea
Series Title: Springer Tracts in Advanced Robotics
DOI: https://doi.org/10.1007/978-3-642-13642-9
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2010
Hardcover ISBN: 978-3-642-13641-2Published: 23 June 2010
Softcover ISBN: 978-3-642-26385-9Published: 05 September 2012
eBook ISBN: 978-3-642-13642-9Published: 15 July 2010
Series ISSN: 1610-7438
Series E-ISSN: 1610-742X
Edition Number: 1
Number of Pages: 160
Number of Illustrations: 35 illustrations in colour
Topics: Robotics and Automation, Pattern Recognition, Artificial Intelligence