  Tracking multiple objects individually differs from tracking groups of
related objects. When an object is a part of the group, its trajectory depends
on the trajectories of the other group members. Most of the current
state-of-the-art trackers follow the approach of tracking each object
independently, with the mechanism to handle the overlapping trajectories where
necessary. Such an approach does not take inter-object relations into account,
which may cause unreliable tracking for the members of the groups, especially
in crowded scenarios, where individual cues become unreliable due to
occlusions. To overcome these limitations and to extend such trackers to
crowded scenes, we propose a plug-in Relation Encoding Module (REM). REM
encodes relations between tracked objects by running a message passing over a
corresponding spatio-temporal graph, computing relation embeddings for the
tracked objects. Our experiments on MOT17 and MOT20 demonstrate that the
baseline tracker improves its results after a simple extension with REM. The
proposed module allows for tracking severely or even fully occluded objects by
utilizing relational cues.
