ML Anthology

Student-tMixture Filter for Robust, Real-Time Visual Tracking

James Loxam, Tom Drummond
ECCV 2008 pp. 372-385
doi:10.1007/978-3-540-88690-7_28 /eccv/2008/loxam2008eccv-student/

Abstract

Filtering is a key problem in modern information theory; from a series of noisy measurement, one would like to estimate the state of some system. A number of solutions exist in the literature, such as the Kalman filter or the various particle and hybrid filters, but each has its drawbacks. In this paper, a filter is introduced based on a mixture of Student- t modes for all distributions, eliminating the need for arbitrary decisions when treating outliers and providing robust real-time operation in a true Bayesian manner.

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Cite

Text

Loxam and Drummond. "Student-tMixture Filter for Robust, Real-Time Visual Tracking." European Conference on Computer Vision, 2008. doi:10.1007/978-3-540-88690-7_28

Markdown

[Loxam and Drummond. "Student-tMixture Filter for Robust, Real-Time Visual Tracking." European Conference on Computer Vision, 2008.](https://mlanthology.org/eccv/2008/loxam2008eccv-student/) doi:10.1007/978-3-540-88690-7_28

BibTeX

@inproceedings{loxam2008eccv-student,
  title     = {{Student-tMixture Filter for Robust, Real-Time Visual Tracking}},
  author    = {Loxam, James and Drummond, Tom},
  booktitle = {European Conference on Computer Vision},
  year      = {2008},
  pages     = {372-385},
  doi       = {10.1007/978-3-540-88690-7_28},
  url       = {https://mlanthology.org/eccv/2008/loxam2008eccv-student/}
}