@InProceedings{	  soto:2005,
  author	= {A. Soto},
  title		= {Self Adaptive Particle Filter},
  booktitle	= {Proceedings of International Join Conference on Artificial
		  Intelligence (IJCAI)},
  pages		= {1398-1406},
  year		= {2005},
  abstract	= {The particle filter has emerged as a useful tool for
		  problems requiring dynamic state estimation. The efficiency
		  and accuracy of the filter depend mostly on the number of
		  particles used in the estimation and on the propagation
		  function used to re-allocate these particles at each
		  iteration. Both features are specified beforehand and are
		  kept fixed in the reg- ular implementation of the filter.
		  In practice this may be highly inappropriate since it
		  ignores errors in the models and the varying dynamics of
		  the pro- cesses. This work presents a self adaptive version
		  of the particle filter that uses statistical methods to
		  adapt the number of particles and the propagation function
		  at each iteration. Furthermore, our method presents similar
		  computational load than the stan- dard particle filter. We
		  show the advantages of the self adaptive filter by applying
		  it to a synthetic ex- ample and to the visual tracking of
		  targets in a real video sequence. },
  url		= {Soto-IJCAI-0 5.pdf}
}

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