  In tracking multiple objects, it is often assumed that each observation
(measurement) is originated from one and only one object. However, we may
encounter a situation that each measurement may or may not be associated with
multiple objects at each time step --spawning. Therefore, the association of
each measurement to multiple objects is a crucial task to perform in order to
track multiple objects with birth and death. In this paper, we introduce a
novel Bayesian nonparametric approach that models a scenario where each
observation may be drawn from an unknown number of objects for which it
provides a tractable Markov chain Monte Carlo (MCMC) approach to sample from
the posterior distribution. The number of objects at each time step, itself, is
also assumed to be unknown. We, then, show through experiments the advantage of
nonparametric modeling to scenarios with spawning events. Our experiment
results also demonstrate the advantages of our framework over the existing
methods.
