Maintaining multi-modality through mixture tracking

In recent years particle filters have become a tremendously popular tool to perform tracking for non-linear and/or non-Gaussian models. This is due to their simplicity, generality and success over a wide range of challenging applications. Particle filters, and Monte Carlo methods in general, are however poor at consistently maintaining the multi-modality of the target distributions that may arise due to ambiguity or the presence of multiple objects. To address this shortcoming this paper proposes to model the target distribution as a non-parametric mixture model, and presents the general tracking recursion in this case. It is shown how a Monte Carlo implementation of the general recursion leads to a mixture of panicle filters that interact only in the computation of the mixture weights, thus leading to an efficient numerical algorithm, where all the results pertaining to standard particle filters apply. The ability of the new method to maintain posterior multi-modality is illustrated on a synthetic example and a real world tracking problem involving the tracking of football players in a video sequence