Multi-object tracking (MOT) emerges as a pivotal and highly promising branch in the field of computer vision. Classical closed-vocabulary MOT (CV-MOT) methods aim to track objects of predefined categories. Recently, some open-vocabulary MOT (OV-MOT) methods have successfully addressed the problem of tracking unknown categories. However, we found that the CV-MOT and OV-MOT methods each struggle to excel in the tasks of the other. In this paper, we present a unified framework, Associate Everything Detected (AED), that simultaneously tackles CV-MOT and OV-MOT by integrating with any off-the-shelf detector and supports unknown categories. Different from existing tracking-by-detection MOT methods, AED gets rid of prior knowledge (e.g. motion cues) and relies solely on highly robust feature learning to handle complex trajectories in OV-MOT tasks while keeping excellent performance in CV-MOT tasks. Specifically, we model the association task as a similarity decoding problem and propose a sim-decoder with an association-centric learning mechanism. The sim-decoder calculates similarities in three aspects: spatial, temporal, and cross-clip. Subsequently, association-centric learning leverages these threefold similarities to ensure that the extracted features are appropriate for continuous tracking and robust enough to generalize to unknown categories. Compared with existing powerful OV-MOT and CV-MOT methods, AED achieves superior performance on TAO, SportsMOT, and DanceTrack without any prior knowledge. Our code is available at https://github.com/balabooooo/AED.

本研究针对多目标跟踪(MOT)领域中的传统封闭词汇跟踪(CV-MOT)和开放词汇跟踪(OV-MOT)各自面临的局限性，提出了统一的框架“关联所有探测到的对象(AED)”。该方法通过引入强大的特征学习，无需先验知识，兼顾CV-MOT和OV-MOT的性能，显著提升了在未知类别跟踪中的表现。最显著的发现是，AED在多个数据集上如TAO、SportsMOT和DanceTrack”等较现有方法表现更优。 
  

关联所有探测到的对象：促进未知场景下的基于检测的跟踪