Multiple target tracking based on sets of trajectories
This work addresses the problem of tracking multiple targets in fields like surveillance or robotics, but it appears incremental as it builds on existing random finite set methods.
The paper tackles the multiple target tracking problem by proposing a Bayesian approach that characterizes the distribution of trajectories using sets of trajectories within the random finite set framework, resulting in a conjugate family of multitrajectory density functions for standard tracking models.
We propose a solution of the multiple target tracking (MTT) problem based on sets of trajectories and the random finite set framework. A full Bayesian approach to MTT should characterise the distribution of the trajectories given the measurements, as it contains all information about the trajectories. We attain this by considering multi-object density functions in which objects are trajectories. For the standard tracking models, we also describe a conjugate family of multitrajectory density functions.