Kernel particle filter: iterative sampling for efficient visual tracking

@inproceedings{Chang2003KernelPF,
  title={Kernel particle filter: iterative sampling for efficient visual tracking},
  author={Cheng Chang and Rashid Ansari},
  booktitle={ICIP},
  year={2003}
}
Particle filter has recently received attention in computer vision applications due to attributes such as its ability to carry multiple hypotheses and its relaxation of the linearity assumption. Its shortcoming is increase in complexity with state dimension. We present kernel panicle filter as a variation of particle filter with improved sampling efficiency and performance in visual tracking. Unlike existing methods that use stochastic or deterministic optimization procedures to find the modes… CONTINUE READING
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