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.

多目标跟踪（MOT）是计算机视觉中的一个重要问题，具有广泛的应用。在单个网络中将 MOT 制定为目标检测和重新识别的多任务学习是有吸引力的，因为它允许两个任务的联合优化并享有高计算效率。然而，我们发现这两个任务往往相互竞争，需要仔细处理。特别是，以前的工作通常将 re-ID 视为次要任务，其准确性受到主要检测任务的严重影响。结果，网络偏向于主要检测任务，这对 re-ID 任务不公平。为了解决这个问题，我们提出了一种简单而有效的方法，称为FairMOT基于无锚对象检测架构 CenterNet。请注意，它不是 CenterNet 和 re-ID 的简单组合。相反，我们提出了一系列详细设计，这些设计对于通过彻底的实证研究获得良好的跟踪结果至关重要。由此产生的方法实现了检测和跟踪的高精度。该方法在几个公共数据集上大大优于最先进的方法。源代码和预训练模型在 https://github.com/ifzhang/FairMOT 上发布。