Sequential Importance Sampling for Visual Tracking Reconsidered
Péter Torma, Csaba Szepesvári · 2003
We consider sequential importance sampling for filtering dynamical systems observed in noise and when the importance function is defined over a few selected components of the state space, typically the components corresponding to the innovation part of the process to be filtered. In this case the basic importance sampling algorithm yields high variance estimates of the posterior. The problem appears for example in visual tracking using particle filters when one uses an importance function based on the output of color blob detector.