  A large-scale multi-object tracker based on the generalised labeled
multi-Bernoulli (GLMB) filter is proposed. The algorithm is capable of tracking
a very large, unknown and time-varying number of objects simultaneously, in the
presence of a high number of false alarms, as well as misdetections and
measurement origin uncertainty due to closely spaced objects. The algorithm is
demonstrated on a simulated large-scale tracking scenario, where the peak
number objects appearing simultaneously exceeds one million. To evaluate the
performance of the proposed tracker, we also introduce a new method of applying
the optimal sub-pattern assignment (OSPA) metric, and an efficient strategy for
its evaluation in large-scale scenarios.
