The key challenge in multiple-object tracking (MOT) task is temporal modeling of the object under track. Existing tracking-by-detection methods adopt simple heuristics, such as spatial or appearance similarity. Such methods, in spite of their commonality, are overly simple and insufficient to model complex variations, such as tracking through occlusion. Inherently, existing methods lack the ability to learn temporal variations from data. In this paper, we present MOTR, the first fully end-to-end multiple-object tracking framework. It learns to model the long-range temporal variation of the objects. It performs temporal association implicitly and avoids previous explicit heuristics. Built on Transformer and DETR, MOTR introduces the concept of "track query". Each track query models the entire track of an object. It is transferred and updated frame-by-frame to perform object detection and tracking, in a seamless manner. Temporal aggregation network combined with multi-frame training is proposed to model the long-range temporal relation. Experimental results show that MOTR achieves state-of-the-art performance. Code is available at https://github.com/megvii-model/MOTR.

本文介绍了一种名为MOTR的方法，它使用track query来建模整个视频中的跟踪实例，并引入了一种新的时域关系建模方法。实验结果表明，MOTR在HOTA指标上比现有技术ByteTrack提高了6.5%，在MOT17测试中，MOTR的关联性能也胜过了TrackFormer和TransTrack。

MOTR: 使用Transformer进行端到端的多目标追踪