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Incremental Learning of Statistical Motion Patterns with Growing Hidden Markov Models

  • Dizan Vasquez
  • Christian Laugier
  • Thierry Fraichard
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 66)

Summary

Modeling and predicting human and vehicle motion is an active research domain. Due to the difficulty of modeling the various factors that determine motion (eg internal state, perception, etc.) 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 (eg camera, laser scanner), and then using that model to predict further motion. Unfortunately, most current techniques use off-line learning algorithms, meaning that they are not able to learn new motion patterns once the learning stage has finished. In this paper, we present an approach which is able to learn new motion patterns incrementally, and in parallel with prediction. Our work is based on a novel extension to Hidden Markov Models called Growing Hidden Markov models.

Keywords

Hide Markov Model Motion Pattern Incremental Learn Observation Sequence Voronoi Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Dizan Vasquez
    • 1
  • Christian Laugier
    • 2
  • Thierry Fraichard
    • 2
  1. 1.ASLSwiss Federal Institute of Technology Zurich 
  2. 2.LIG/CNRSINRIA Rhone-Alpes France 

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