Poisson multi-Bernoulli mixture trackers: continuity through random finite sets of trajectories
For researchers in multi-target tracking, this work provides theoretical continuity for the PMBM filter, which was previously lacking, but the contribution is incremental as it builds on existing RFS of trajectories formulation.
The paper derives two trajectory random finite set filters (PMBM trackers) that efficiently estimate the set of trajectories, establishing that the PMBM filter provides continuity between time steps similar to the multiple hypothesis tracker.
The Poisson multi-Bernoulli mixture (PMBM) is an unlabelled multi-target distribution for which the prediction and update are closed. It has a Poisson birth process, and new Bernoulli components are generated on each new measurement as a part of the Bayesian measurement update. The PMBM filter is similar to the multiple hypothesis tracker (MHT), but seemingly does not provide explicit continuity between time steps. This paper considers a recently developed formulation of the multi-target tracking problem as a random finite set (RFS) of trajectories, and derives two trajectory RFS filters, called PMBM trackers. The PMBM trackers efficiently estimate the set of trajectories, and share hypothesis structure with the PMBM filter. By showing that the prediction and update in the PMBM filter can be viewed as an efficient method for calculating the time marginals of the RFS of trajectories, continuity in the same sense as MHT is established for the PMBM filter.