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.

该研究提出了一种名为 RelationTrack 的 MOT 框架，它使用全局上下文分离模块和引导变压器编码器来解决检测和重新识别的矛盾，并通过在性能上显著优于现有的算法来建立了新的最先进性能水平。

RelationTrack: 带有解耦表示的关系感知多目标跟踪

RelationTrack: Relation-aware Multiple Object Tracking with Decoupled  Representation

Modern online multiple object tracking (MOT) methods usually focus on two
directions to improve tracking performance. One is to predict new positions in
an incoming frame based on tracking information from previous frames, and the
other is to enhance data association by generating more discriminative identity
embeddings. Some works combined both directions within one framework but
handled them as two individual tasks, thus gaining little mutual benefits. In
this paper, we propose a novel unified model with synergy between position
prediction and embedding association. The two tasks are linked by
temporal-aware target attention and distractor attention, as well as
identity-aware memory aggregation model. Specifically, the attention modules
can make the prediction focus more on targets and less on distractors,
therefore more reliable embeddings can be extracted accordingly for
association. On the other hand, such reliable embeddings can boost
identity-awareness through memory aggregation, hence strengthen attention
modules and suppress drifts. In this way, the synergy between position
prediction and embedding association is achieved, which leads to strong
robustness to occlusions. Extensive experiments demonstrate the superiority of
our proposed model over a wide range of existing methods on MOTChallenge
benchmarks. Our code and models are publicly available at
this https URL

本研究提出了一种新的联合模型，将位置预测和嵌入关联相结合，以尽量从目标信息中提取更可靠的嵌入，从而提高数据关联的准确性和鲁棒性。实验结果表明，该方法在 MOT 挑战基准测试中的性能优于现有方法。