In this work, we propose TransTrack, a simple but efficient scheme to solve
the multiple object tracking problems. TransTrack leverages the transformer
architecture, which is an attention-based query-key mechanism. It applies
object features from the previous frame as a query of the current frame and
introduces a set of learned object queries to enable detecting new-coming
objects. It builds up a novel joint-detection-and-tracking paradigm by
accomplishing object detection and object association in a single shot,
simplifying complicated multi-step settings in tracking-by-detection methods.
On MOT17 and MOT20 benchmark, TransTrack achieves 74.5\% and 64.5\% MOTA,
respectively, competitive to the state-of-the-art methods. We expect TransTrack
to provide a novel perspective for multiple object tracking. The code is
available at: https://github.com/PeizeSun/TransTrack.

本文提出了 TransTrack，该方法利用 Transformer 架构解决多目标跟踪问题，采用对象特征作为当前帧的查询并引入一组学习对象查询以检测新对象，通过实现单次检测和跟踪建立了新的联合检测和跟踪范例，在 MOT17 和 MOT20 基准测试中达到了 74.5％和 64.5％的 MOTA，是一种优秀的多目标跟踪方法。