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Chapitre D'ouvrage Année : 2011

Incremental Learning of Statistical Motion Patterns with Growing Hidden Markov Models

Résumé

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.

Dates et versions

inria-00584320 , version 1 (08-04-2011)

Identifiants

Citer

Dizan Alejandro Vasquez Govea, Thierry Fraichard, Christian Laugier. Incremental Learning of Statistical Motion Patterns with Growing Hidden Markov Models. Makoto Kaneko and Yoshihiko Nakamura. Robotics Research, 66, Springer Berlin, pp.75-86, 2011, Springer Tracts in Advanced Robotics, 978-3-642-14742-5. ⟨10.1007/978-3-642-14743-2_7⟩. ⟨inria-00584320⟩
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