Abstract
Hidden Markov Models are widely used in forecasting the unknown sequence based on observation on outside system. In this paper, they are applied in Human Motion Recognition. With the human’s silhouettes, the paper mainly deals with how to get the models of regular actions and combine them with HMM to recognize the motions of motive people. As for the localization on gray images of silhouettes, an algorithm combining silhouette contrasting and centroid tracking is put forward. The results show that the new algorithm has better performance.
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© 2007 Springer-Verlag Berlin Heidelberg
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Xiong, J., Liu, Z. (2007). Human Motion Recognition Based on Hidden Markov Models. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_51
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DOI: https://doi.org/10.1007/978-3-540-74581-5_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74580-8
Online ISBN: 978-3-540-74581-5
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