Abstract
The Product of Hidden Markov Models (PoHMM) is a mixed graphical model defining a probability distribution on a sequence space from the normalized product of several simple Hidden Markov Models (HMMs). Here, we use this model to approach the human action recognition task incorporating mixture-Gaussian output distributions. PoHMM allow us to consider context at different range and to model different dynamics corresponding to different body parts in an efficient way. For estimating the normalization constant Z we introduce the annealed importance sampling (AIS) method in the context of PoHMM in order to obtain no-relative estimates of Z. We compare our approach with one based on fitting a logistic regression model to each two PoHMMs.
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References
Mendoza, M.A., Pérez de la Blanca, N.: Applying space state models in human action recognition: A comparative study. In: Perales, F.J., Fisher, R.B. (eds.) AMDO 2008. LNCS, vol. 5098, pp. 53–62. Springer, Heidelberg (2008)
Brown, A., Hinton, G.E.: Products of hidden markov models. Artificial Intelligence and Statistics, 3–11 (2001)
Neal, R.M.: Annealed importance sampling. Statistics and Computing 11(2), 125–139 (1998)
Rabiner, L.: A tutorial on hidden markov models and selected applications in speech recognition. Proc. of the IEEE 77(2), 257–286 (1989)
Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Computation 14(8), 1771–1800 (2002)
Salakhutdinov, R., Murray, I.: On the quantitative analysis of deep belief networks. In: Proceedings of the Int. conf. on Machine Learning, vol. 25, pp. 872–879 (2008)
Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local svm approach. Pattern Recognition 3(1), 32–36 (2004)
Efros, A.A., Berg, A.C., Mori, G., Malik, J.: Recognizing action at a distance. Computer Vision 2, 726–733 (2003)
Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363–370. Springer, Heidelberg (2003)
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Mendoza, M.Á., Pérez de la Blanca, N., Marín-Jiménez, M.J. (2009). Fitting Product of HMM to Human Motions. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_100
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DOI: https://doi.org/10.1007/978-3-642-03767-2_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03766-5
Online ISBN: 978-3-642-03767-2
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