  Multi-object tracking (MOT) is the task of estimating the state trajectories
of an unknown and time-varying number of objects over a certain time window.
Several algorithms have been proposed to tackle the multi-object smoothing
task, where object detections can be conditioned on all the measurements in the
time window. However, the best-performing methods suffer from intractable
computational complexity and require approximations, performing suboptimally in
complex settings. Deep learning based algorithms are a possible venue for
tackling this issue but have not been applied extensively in settings where
accurate multi-object models are available and measurements are
low-dimensional. We propose a novel DL architecture specifically tailored for
this setting that decouples the data association task from the smoothing task.
We compare the performance of the proposed smoother to the state-of-the-art in
different tasks of varying difficulty and provide, to the best of our
knowledge, the first comparison between traditional Bayesian trackers and DL
trackers in the smoothing problem setting.
