  Transformers have proven superior performance for a wide variety of tasks
since they were introduced. In recent years, they have drawn attention from the
vision community in tasks such as image classification and object detection.
Despite this wave, an accurate and efficient multiple-object tracking (MOT)
method based on transformers is yet to be designed. We argue that the direct
application of a transformer architecture with quadratic complexity and
insufficient noise-initialized sparse queries - is not optimal for MOT. We
propose TransCenter, a transformer-based MOT architecture with dense
representations for accurately tracking all the objects while keeping a
reasonable runtime. Methodologically, we propose the use of image-related dense
detection queries and efficient sparse tracking queries produced by our
carefully designed query learning networks (QLN). On one hand, the dense
image-related detection queries allow us to infer targets' locations globally
and robustly through dense heatmap outputs. On the other hand, the set of
sparse tracking queries efficiently interacts with image features in our
TransCenter Decoder to associate object positions through time. As a result,
TransCenter exhibits remarkable performance improvements and outperforms by a
large margin the current state-of-the-art methods in two standard MOT
benchmarks with two tracking settings (public/private). TransCenter is also
proven efficient and accurate by an extensive ablation study and comparisons to
more naive alternatives and concurrent works. For scientific interest, the code
is made publicly available at https://github.com/yihongxu/transcenter.
