  In this paper, we demonstrate that deep learning based method can be used to
fuse multi-object densities. Given a scenario with several sensors with
possibly different field-of-views, tracking is performed locally in each sensor
by a tracker, which produces random finite set multi-object densities. To fuse
outputs from different trackers, we adapt a recently proposed transformer-based
multi-object tracker, where the fusion result is a global multi-object density,
describing the set of all alive objects at the current time. We compare the
performance of the transformer-based fusion method with a well-performing
model-based Bayesian fusion method in several simulated scenarios with
different parameter settings using synthetic data. The simulation results show
that the transformer-based fusion method outperforms the model-based Bayesian
method in our experimental scenarios.
