Bayesian Multi-object Tracking: Probability Hypothesis Density Filter and Beyond

This talk provides an overview of the PHD filter and how the same RFS framework can be used to address multi-object trajectory estimation. By using labels to distinguish individual trajectories, this approach admits MOT filters that alleviate integration over multiple scans and enables modeling/estimation of ancestry for spawning objects. Labeled RFS posterior/filtering densities are closed under truncation and admit analytic truncation errors critical for numerical approximations.

IEEE-Affiliated Group Name: The IEEE Signal Processing Society

URL: https://rc.signalprocessingsociety.org/education/webinars/SPSWEB2201.html