Heavy-Tailed model for visual tracking via robust subspace learning
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
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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.