We present a novel transformer-based architecture for global multi-object
tracking. Our network takes a short sequence of frames as input and produces
global trajectories for all objects. The core component is a global tracking
transformer that operates on objects from all frames in the sequence. The
transformer encodes object features from all frames, and uses trajectory
queries to group them into trajectories. The trajectory queries are object
features from a single frame and naturally produce unique trajectories. Our
global tracking transformer does not require intermediate pairwise grouping or
combinatorial association, and can be jointly trained with an object detector.
It achieves competitive performance on the popular MOT17 benchmark, with 75.3
MOTA and 59.1 HOTA. More importantly, our framework seamlessly integrates into
state-of-the-art large-vocabulary detectors to track any objects. Experiments
on the challenging TAO dataset show that our framework consistently improves
upon baselines that are based on pairwise association, outperforming published
works by a significant 7.7 tracking mAP. Code is available at
this https URL

本论文提出了一种新型基于 Transformer 的架构，用于全局多目标跟踪，通过对各帧图像上的物体特征进行编码，通过路径查询将其分组成轨迹，与目标检测器联合训练，可以实现对任意对象的跟踪，并在 MOT17 基准测试中实现了 75.3 的 MOTA 和 59.1 的 HOTA，超越了基于成对联想的基线，并在 TAO 数据集上得到显著的 7.7 跟踪 mAP 的提高。