Enhancing Particle Filters using Local Likelihood Sampling. Torma, P. & Szepesvári, C. In ECCV, pages 16–27, 2004.
Enhancing Particle Filters using Local Likelihood Sampling [pdf]Paper  abstract   bibtex   
Particle filters provide a means to track the state of an object even when the dynamics and the observations are non-linear/non-Gaussian. However, they can be very inefficient when the observation noise is low as compared to the system noise, as it is often the case in visual tracking applications. In this paper we propose a new two-stage sampling procedure to boost the performance of particle filters under this condition. The new procedure is shown to reduce the variance of the weights by means of a theoretical analysis. This result is confirmed in a series of synthetic and real-world visual tracking experiments.

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