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Human Motion Tracking Based on Markov Random Field and Hopfield Neural Network

  • Zhihui Li
  • Fenggang Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

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.

Keywords

Joint Angle Motion Model Human Motion Markov Random Field Joint Position 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhihui Li
    • 1
  • Fenggang Huang
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Engineering UniversityHarbinChina

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