  Multi-object tracking (MOT) is an important problem in computer vision which
has a wide range of applications. Formulating MOT as multi-task learning of
object detection and re-ID in a single network is appealing since it allows
joint optimization of the two tasks and enjoys high computation efficiency.
However, we find that the two tasks tend to compete with each other which need
to be carefully addressed. In particular, previous works usually treat re-ID as
a secondary task whose accuracy is heavily affected by the primary detection
task. As a result, the network is biased to the primary detection task which is
not fair to the re-ID task. To solve the problem, we present a simple yet
effective approach termed as FairMOT based on the anchor-free object detection
architecture CenterNet. Note that it is not a naive combination of CenterNet
and re-ID. Instead, we present a bunch of detailed designs which are critical
to achieve good tracking results by thorough empirical studies. The resulting
approach achieves high accuracy for both detection and tracking. The approach
outperforms the state-of-the-art methods by a large margin on several public
datasets. The source code and pre-trained models are released at
https://github.com/ifzhang/FairMOT.
