Abstract
In this paper we address the problem of human action recognition from a single training sequence per class using a modified version of the Hidden Markov Model. Inspired by codebook approaches in object and scene categorization, we first construct a codebook of possible discrete observations by applying a clustering algorithm to all samples from all classes. The number of clusters defines the size of the codebook. Given a new observation, we assign to it a probability to belong to every cluster, i.e., to correspond to a discrete value of the codebook. In this sense, we change the ‘winner takes all’ rule in the discrete-observation HMM for a distributed probability of membership. It implies the modification of the Baum-Welch algorithm for training discrete HMM to be able to deal with fuzzy observations. We compare our approach with other models such as, dynamic time warping (DTW), continuous-observation HMM, Conditional Random Fields (CRF) and Hidden Conditional Random Fields (HCRF) for human action recognition.
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References
Huda, S., Yearwood, J., Togneri, R.: A Constraint-Based Evolutionary Learning Approach to the Expectation Maximization for Optimal Estimation of the Hidden Markov Model for Speech Signal Modelling. IEEE Trans. Syst., Man, Cyber. B, Cybern. 39(1), 182–197 (2009)
Quattoni, A., Wang, S.B., Morency, L.-P., Collins, M., Darrell, T.: Hidden Conditional Random Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(10), 1848–1852 (2007)
Lafferty, J., McCallum, A., Pereira, F.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: Proceedings 18th International Conf. on Machine Learning, pp. 282–289 (2001)
Gunawardana, A., Mahajan, M., Acero, A., Platt, J.C.: Hidden conditional random fields for phone classification. In: International Conference on Speech Communication and Technology, pp. 1117–1120 (2005)
Zhang, J., Gong, S.: Action categorization with modified hidden conditional random field. Pattern Recognition 43(1), 197–203 (2010)
Fei-Fei, L., Fergus, R., Perona, P.: One-Shot Learning of Object Categories. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 594–611 (2006)
Yang, W., Wang, Y., Mori, G.: Human Action Recognition from a Single Clip per Action. In: 2nd International Workshop on Machine Learning for Vision-based Motion Analysis (2009)
Seo, H.J., Milanfar, P.: Action Recognition from One Example. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(5), 867–882 (2011)
Ryoo, M.S., Yu, W.: One Video is Sufficient? Human Activity Recognition Using Active Video Composition. In: WACV 2011 (2011)
Liang, Y.-M., Shih, S.-W., Shih, A.C.-C., Liao, H.-Y.M., Lin, C.-C.: Learning Atomic Human Actions Using Variable-Length Markov Models. IEEE Trans. Syst., Man, Cyber. B, Cybern. 39(1), 268–280 (2009)
Niebles, J.C., Wang, H., Fei-Fei, L.: Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words. IJCV 79(3) (September 2008)
Schuldt, C., Laptev, I., Caputo, B.: Recognizing Human Actions: a Local SVM Approach. In: ICPR 2004 (2004)
Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior Recognition via Sparse Spatio- Temporal Features. In: IEEE Workshop on VS-PETS 2005 (2005)
Bobick, A.F., Davis, J.W.: The Recognition of Human Movement Using Temporal Templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(3), 257–267 (2001)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 257–286 (1989)
Uguz, H., Ozturk, A., Saracoglu, R., Arslan, A.: A biomedical system based on fuzzy discrete hidden Markov model for the diagnosis of the brain diseases. Expert Systems with Applications 35, 1104–1114 (2008)
Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as Space-Time Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(12), 2247–2253 (2007)
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Orrite, C., Rodríguez, M., Montañés, M. (2011). One-Sequence Learning of Human Actions. In: Salah, A.A., Lepri, B. (eds) Human Behavior Understanding. HBU 2011. Lecture Notes in Computer Science, vol 7065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25446-8_5
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DOI: https://doi.org/10.1007/978-3-642-25446-8_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25445-1
Online ISBN: 978-3-642-25446-8
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