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 …

We provide a derivation of the Poisson multi-Bernoulli mixture (PMBM) filter for multitarget
tracking with the standard point target measurements without using probability generating …

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 …

Complex track stitching problems have risen in prominence with the development of wide
area persistent sensors for urban surveillance. Common approaches such as multiple …

Multiple hypothesis tracking (MHT) addresses difficult tracking problems by maintaining
alternative association hypotheses until enough good data, eg, features, are collected to …

Tracking algorithm takes as input a list of clustered laserscanner measurements (objects),
which for simplicity will be called measurements. The tracking algorithm forms a track …

Performance evaluations of multi-target tracking algorithms are often limited to consider
comparisons within the same algorithm family. In this paper, two conceptually different multi …

In most hypothesis-oriented Multiple Hypothesis Tracking (MHT) implementations, the target-
to-measurement data association is typically solved by using the Murty's algorithm …

This thesis is concerned with three important components in target tracking, namely multiple-
model filtering, data association and sensor resolution modeling. For multiple-model …

This paper describes three mathematical formalisms for defining multiple target tracking
(MTT) problems and deriving multiple hypothesis tracking (MHT) algorithms. The three …