On using likelihood-adjusted proposals in particle filtering: local importance sampling
Particle filters provide a means to track the state of an object even when the dynamics and the observations are non-linear/nonGaussian. 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. We provide conditions under which the new procedure is proven to reduce the variance of the weights. Synthetic and real-world visual tracking experiments are used to confirm the validity of the theoretical analysis.