This tutorial introduces state-of-the-art methods for tracking multiple spatially extended
objects based on unlabeled noisy point clouds, eg, from radar or lidar sensors. In the first …

Multiple hypothesis tracking addresses difficult multiple target tracking problems by making
association decisions using multiple scans or frames of data. This paper reviews forty years …

This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for multi-target
tracking: one to estimate the set of alive trajectories at each time step and another to …

In this article, we propose a metric on the space of finite sets of trajectories for assessing
multi-target tracking algorithms in a mathematically sound way. The main use of the metric is …

This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter for coexisting point and
extended targets, ie, for scenarios where there may be simultaneous point and extended …

In this article, we demonstrate how a variation of the joint integrated probabilistic data
association with interacting multiple models and a visibility state can be derived as a special …

The Poisson multi-Bernoulli mixture (PMBM) is a multi-target distribution for which the
prediction and update are closed. By applying the random finite set (RFS) framework to multi …

In this article, we propose a Poisson multi-Bernoulli (PMB) filter for extended object tracking
(EOT), which directly estimates the set of object trajectories, using belief propagation (BP) …

Multi-object tracking (MOT) is the task of estimating the state trajectories of an unknown and
time-varying number of objects over a certain time window. Several algorithms have been …

This paper provides a comparative analysis between the adaptive birth model used in the
labelled random finite set literature and the track initiation in the Poisson multi-Bernoulli …