  Transformer networks have proven extremely powerful for a wide variety of
tasks since they were introduced. Computer vision is not an exception, as the
use of transformers has become very popular in the vision community in recent
years. Despite this wave, multiple-object tracking (MOT) exhibits for now some
sort of incompatibility with transformers. We argue that the standard
representation -- bounding boxes -- is not adapted to learning transformers for
MOT. Inspired by recent research, we propose TransCenter, the first
transformer-based architecture for tracking the centers of multiple targets.
Methodologically, we propose the use of dense queries in a double-decoder
network, to be able to robustly infer the heatmap of targets' centers and
associate them through time. TransCenter outperforms the current
state-of-the-art in multiple-object tracking, both in MOT17 and MOT20. Our
ablation study demonstrates the advantage in the proposed architecture compared
to more naive alternatives. The code will be made publicly available.
