  Multiple-object tracking(MOT) is mostly dominated by complex and multi-step
tracking-by-detection algorithm, which performs object detection, feature
extraction and temporal association, separately. Query-key mechanism in
single-object tracking(SOT), which tracks the object of the current frame by
object feature of the previous frame, has great potential to set up a simple
joint-detection-and-tracking MOT paradigm. Nonetheless, the query-key method is
seldom studied due to its inability to detect new-coming objects. In this work,
we propose TransTrack, a baseline for MOT with Transformer. It takes advantage
of query-key mechanism and introduces a set of learned object queries into the
pipeline to enable detecting new-coming objects. TransTrack has three main
advantages: (1) It is an online joint-detection-and-tracking pipeline based on
query-key mechanism. Complex and multi-step components in the previous methods
are simplified. (2) It is a brand new architecture based on Transformer. The
learned object query detects objects in the current frame. The object feature
query from the previous frame associates those current objects with the
previous ones. (3) For the first time, we demonstrate a much simple and
effective method based on query-key mechanism and Transformer architecture
could achieve competitive 65.8\% MOTA on the MOT17 challenge dataset. We hope
TransTrack can provide a new perspective for multiple-object tracking. The code
is available at: \url{https://github.com/PeizeSun/TransTrack}.
