An Adaptive Learning Rate GMM for Background Extraction

@article{Sheng2008AnAL,
  title={An Adaptive Learning Rate GMM for Background Extraction},
  author={Zunbing Sheng and Xianyu Cui},
  journal={2008 International Conference on Computer Science and Software Engineering},
  year={2008},
  volume={6},
  pages={174-176}
}
The rapidness and stability of background extraction from image sequences are incompatible, when a conventional Gaussian mixture models is used to rebuild background. If the background region of the scene is changed, the extracted background becomes bad until the transition is over. A novelty adaptive method is presented to adjust learning rate of GMM in Hilbert space. Background extraction is treated as the process of approaching to certain point in Hilbert space, so the real-time learning… CONTINUE READING

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Color model selection and adaption in dynamic scenes ” , Computer VisionECCV ' 98 5 th European Conference on Computer Vision

  • Y. Raja, J. McKennaS., S. Gong
  • Proceedings
  • 1998

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