Poisson Multi-Bernoulli Mixtures for Sets of Trajectories
This work addresses multi-target tracking for surveillance applications, offering an incremental extension of PMBM to trajectory sets.
The paper tackled the problem of tracking multiple targets over time by extending the Poisson Multi-Bernoulli Mixture (PMBM) density to sets of trajectories, showing it is conjugate and developing filters that provide optimal probabilistic information. The result is state-of-the-art performance in simulations compared to other multi-target tracking algorithms.
The Poisson Multi-Bernoulli Mixture (PMBM) density is a conjugate multi-target density for the standard point target model with Poisson point process birth. This means that both the filtering and predicted densities for the set of targets are PMBM. In this paper, we first show that the PMBM density is also conjugate for sets of trajectories with the standard point target measurement model. Second, based on this theoretical foundation, we develop two trajectory PMBM filters that provide recursions to calculate the posterior density for the set of all trajectories that have ever been present in the surveillance area, and the posterior density of the set of trajectories present at the current time step in the surveillance area. These two filters therefore provide complete probabilistic information on the considered trajectories enabling optimal trajectory estimation. Third, we establish that the density of the set of trajectories in any time window, given the measurements in a possibly different time window, is also a PMBM. Finally, the trajectory PMBM filters are evaluated via simulations, and are shown to yield state-of-the-art performance compared to other multi-target tracking algorithms based on random finite sets and multiple hypothesis tracking.