Abstract
This paper presents a method of human motion tracking based on Markov random field and Hopfield neural networks. The model of rigid body motion is first introduced in the MRF-based motion segmentation. The potential function in MRF is defined according to this motion model. The Hopfield neural network is first used in the implementation of MRF to take advantage of some mature Neural Network technique. After the introduction of the model of rigid body motion the joint angles of human body can be estimated .It is also helpful to the estimation of the proportions of human body, which is significant to the accurate estimation of human motion. Finally the experimental results are given and the existed problems in this method are pointed out.
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© 2006 Springer-Verlag Berlin Heidelberg
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Li, Z., Huang, F. (2006). Human Motion Tracking Based on Markov Random Field and Hopfield Neural Network. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_60
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DOI: https://doi.org/10.1007/11760023_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
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