Recent advances in Multi-Object Tracking (MOT) have achieved remarkable
success in short-term association within the decoupled tracking-by-detection
online paradigm. However, long-term tracking still remains a challenging task.
Although graph-based approaches can address this issue by modeling trajectories
as a graph in the decoupled manner, their non-online nature poses obstacles for
real-time applications. In this paper, we demonstrate that the trajectory graph
is a directed acyclic graph, which can be represented by an object sequence
arranged by frame and a binary adjacency matrix. It is a coincidence that the
binary matrix matches the attention mask in the Transformer, and the object
sequence serves exactly as a natural input sequence. Intuitively, we propose
that a pure Transformer can naturally unify short- and long-term associations
in a decoupled and online manner. Our experiments show that a classic
Transformer architecture naturally suits the association problem and achieves a
strong baseline compared to existing foundational methods across four datasets:
DanceTrack, SportsMOT, MOT17, and MOT20, as well as superior generalizability
in domain shift. Moreover, the decoupled property also enables efficient
training and inference. This work pioneers a promising Transformer-based
approach for the MOT task, and provides code to facilitate further research.
this https URL

最近的多目标追踪 (Multi-Object Tracking, MOT) 的前沿研究通过解耦跟踪 - 检测在线范式在短期关联方面取得了显著的成功，但是长期追踪仍然是一个具有挑战性的任务。本论文提出了一种基于 Transformer 的方法，它可以自然地将短期和长期关联解耦并在线进行，通过模型化轨迹为有向无环图，并用二元邻接矩阵表示，实验证明这种方法在多个数据集上取得了强大的基线性能，并在领域迁移方面具有优势，同时由于解耦性质，训练和推理也更加高效。