  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 mixture (PMBM) filter, with point-target models.
The PMBM track initiation is obtained via Bayes' rule applied on the predicted
PMBM density, and creates one Bernoulli component for each received
measurement, representing that this measurement may be clutter or a detection
from a new target. Adaptive birth mimics this procedure by creating a Bernoulli
component for each measurement using a different rule to determine the
probability of existence and a user-defined single-target density. This paper
first provides an analysis of the differences that arise in track initiation
based on isolated measurements. Then, it shows that adaptive birth
underestimates the number of objects present in the surveillance area under
common modelling assumptions. Finally, we provide numerical simulations to
further illustrate the differences.
