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Growing Hidden Markov Models: An Incremental Tool for Learning and Predicting Human and Vehicle Motion

Swiss Federal Institute of Technology, Zurich, Switzerland, vasquez{at}mavt.ethz.ch

Thierry Fraichard

INRIA, CNRS/LIG and Grenoble University Grenoble, France

Christian Laugier

INRIA, CNRS/LIG and Grenoble University Grenoble, France

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. 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.

Key Words: motion prediction • hidden Markov models • structure learning.

This version was published on November 1, 2009

The International Journal of Robotics Research, Vol. 28, No. 11-12, 1486-1506 (2009)
DOI: 10.1177/0278364909342118


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