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Efficient Non-linear Markov Models for Human Motion

MPG-Autoren
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Lehrmann,  Andreas
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Gehler,  Peter
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Nowozin,  Sebastian
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Zitation

Lehrmann, A., Gehler, P., & Nowozin, S. (2014). Efficient Non-linear Markov Models for Human Motion. In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014) (978-1-4799-5117-8, pp. 1314-1321). Los Alamitos, CA: IEEE Computer Society. doi:10.1109/CVPR.2014.171.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0024-C7D7-4
Zusammenfassung
Dynamic Bayesian networks such as Hidden Markov Models (HMMs) are successfully used as probabilistic models for human motion. The use of hidden variables makes them expressive models, but inference is only approximate and requires procedures such as particle filters or Markov chain Monte Carlo methods. In this work we propose to instead use simple Markov models that only model observed quantities. We retain a highly expressive dynamic model by using interactions that are nonlinear and non-parametric. A presentation of our approach in terms of latent variables shows logarithmic growth for the computation of exact loglikelihoods in the number of latent states. We validate our model on human motion capture data and demonstrate state-of-the-art performance on action recognition and motion completion tasks.