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.

多目标跟踪是估计一定时间窗口内未知数量对象的状态轨迹的任务。本文提出了一种针对此场景的新型深度学习体系结构，将数据关联任务与平滑任务解耦，比较了提出的平滑器与传统贝叶斯跟踪器及深度学习跟踪器在不同任务中的性能。