Growing hidden markov models: An incremental tool for learning and predicting human and vehicle motion

  • Vasquez D
  • Fraichard T
  • Laugier C
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Abstract

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) 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 where motion patterns can be learned incrementally,
and in parallel with prediction. Our work is based on a novel extension
to Hidden Markov Models �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 evaluated
using 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.

Author-supplied keywords

  • Hidden Markov models
  • Motion prediction
  • Structure learning

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