  Existing online multiple object tracking (MOT) algorithms often consist of
two subtasks, detection and re-identification (ReID). In order to enhance the
inference speed and reduce the complexity, current methods commonly integrate
these double subtasks into a unified framework. Nevertheless, detection and
ReID demand diverse features. This issue would result in an optimization
contradiction during the training procedure. With the target of alleviating
this contradiction, we devise a module named Global Context Disentangling (GCD)
that decouples the learned representation into detection-specific and
ReID-specific embeddings. As such, this module provides an implicit manner to
balance the different requirements of these two subtasks. Moreover, we observe
that preceding MOT methods typically leverage local information to associate
the detected targets and neglect to consider the global semantic relation. To
resolve this restriction, we develop a module, referred to as Guided
Transformer Encoder (GTE), by combining the powerful reasoning ability of
Transformer encoder and deformable attention. Unlike previous works, GTE avoids
analyzing all the pixels and only attends to capture the relation between query
nodes and a few self-adaptively selected key samples. Therefore, it is
computationally efficient. Extensive experiments have been conducted on the
MOT16, MOT17 and MOT20 benchmarks to demonstrate the superiority of the
proposed MOT framework, namely RelationTrack. The experimental results indicate
that RelationTrack has surpassed preceding methods significantly and
established a new state-of-the-art performance, e.g., IDF1 of 70.5% and MOTA of
67.2% on MOT20.
