Sparse Bayesian learning for efficient visual tracking

@article{Williams2005SparseBL,
  title={Sparse Bayesian learning for efficient visual tracking},
  author={Oliver Williams and Andrew Blake and Roberto Cipolla},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2005},
  volume={27},
  pages={1292-1304}
}
This paper extends the use of statistical learning algorithms for object localization. It has been shown that object recognizers using kernel-SVMs can be elegantly adapted to localization by means of spatial perturbation of the SVM. While this SVM applies to each frame of a video independently of other frames, the benefits of temporal fusion of data are well-known. This is addressed here by using a fully probabilistic relevance vector machine (RVM) to generate observations with Gaussian… CONTINUE READING
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