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
this https URL

本研究提出了一种基于中心点检测架构的叫做 FairMOT 的简单而有效的方法来解决目标检测和 Re-ID 之间的竞争问题，该方法在多目标跟踪方面表现出色，在多个公共数据集上均优于现有技术。

关于多目标跟踪中检测和重新识别的公平性问题的研究

FairMOT: On the Fairness of Detection and Re-Identification in Multiple  Object Tracking

Person search aims at localizing and identifying a query person from a
gallery of uncropped scene images. Different from person re-identification
(re-ID), its performance also depends on the localization accuracy of a
pedestrian detector. The state-of-the-art methods train the detector
individually, and the detected bounding boxes may be sub-optimal for the
following re-ID task. To alleviate this issue, we propose a re-ID driven
localization refinement framework for providing the refined detection boxes for
person search. Specifically, we develop a differentiable ROI transform layer to
effectively transform the bounding boxes from the original images. Thus, the
box coordinates can be supervised by the re-ID training other than the original
detection task. With this supervision, the detector can generate more reliable
bounding boxes, and the downstream re-ID model can produce more discriminative
embeddings based on the refined person localizations. Extensive experimental
results on the widely used benchmarks demonstrate that our proposed method
performs favorably against the state-of-the-art person search methods.

本研究提出了一种基于 re-ID 驱动的定位精化框架，通过可微的 ROI 转换层有效地转换原始图像中的边界框，实现对检测框的监督，从而提高行人搜索的效果。实验结果显示，该方法在常用的基准测试中对比现有方法表现明显更好。