  Robust data association is critical for analysis of long-term motion
trajectories in complex scenes. In its absence, trajectory precision suffers
due to periods of kinematic ambiguity degrading the quality of follow-on
analysis. Common optimization-based approaches often neglect uncertainty
quantification arising from these events. Consequently, we propose the Joint
Posterior Tracker (JPT), a Bayesian multi-object tracking algorithm that
robustly reasons over the posterior of associations and trajectories. Novel,
permutation-based proposals are crafted for exploration of posterior modes that
correspond to plausible association hypotheses. JPT exhibits more accurate
uncertainty representation of data associations with superior performance on
standard metrics when compared to existing baselines. We also show the utility
of JPT applied to automatic scheduling of user-in-the-loop annotations for
improved trajectory quality.
