Incremental Learning of Statistical Motion Patterns with Growing Hidden Markov Models
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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.
KeywordsHide Markov Model Motion Pattern Incremental Learn Observation Sequence Voronoi Region
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- 2.Dee, H.-M.: Explaining Visible Behaviour. PhD thesis, University of Leeds (2005)Google Scholar
- 4.Jockusch, J., Ritter, H.: An instantaneous topological map for correlated stimuli. In: Proc. of the International Joint Conference on Neural Networks, Washington, US, July 1999, vol. 1, pp. 529–534 (1999)Google Scholar
- 5.Johnson, N., Hogg, D.: Learning the distribution of object trajectories for event recognition. In: Proc. of the British Machine Vision Conference, September 1995, vol. 2, pp. 583–592 (1995)Google Scholar
- 7.Makris, D., Ellis, T.: Spatial and probabilistic modelling of pedestrian behavior. In: Proc. of the British Machine Vision Conference, Cardiff, UK, pp. 557–566 (2002)Google Scholar
- 8.Neal, R.M., Hinton, G.E.: A new view of the em algorithm that justifies incremental, sparse and other variants. In: Jordan, M.I. (ed.) Learning in Graphical Models, pp. 355–368. Kluwer Academic Publishers, Dordrecht (1998)Google Scholar
- 9.Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Readings in speech recognition, 267–296 (1990)Google Scholar
- 11.Vasquez, D.: Incremental Learning for Motion Prediction of Pedestrians and Vehicles. PhD thesis, Institut National Polytechnique de Grenoble, Grenoble, FR (February 2007)Google Scholar
- 12.Vasquez, D., Fraichard, T.: Intentional motion on-line learning and prediction. In: Field and Service Robotics, Port Douglas, Australia (July 2005)Google Scholar
- 13.Walter, M., Psarrow, A., Gong, S.: Learning prior and observation augmented density models for behaviour recognition. In: Proc. of the British Machine Vision Conference, pp. 23–32 (1999)Google Scholar