An information theoretic rule for sample size adaptation in particle filtering

Abstract

To become robust, a tracking algorithm must be able to support uncertainty and ambiguity often inherently present in the data in form of occlusion and clutter. This comes usually at the price of more demanding computations. Sampling methods, such as the popular particle filter, accommodate this capability and provide a means of controlling the computational trade-off by adapting their resolution. This paper presents a method for adapting resolution on-the-fly to current demands. The key idea is to select the number of samples necessary to populate the high probability regions with a predefined density. The scheme then allocates more particles when uncertainty is high while saving resources otherwise. The resulting tracker propagates compact while consistent representations and enables for reliable real time operation otherwise compromised.