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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Loxam, J., Drummond, T. (2008). Student-t Mixture Filter for Robust, Real-Time Visual Tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88690-7_28
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DOI: https://doi.org/10.1007/978-3-540-88690-7_28
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