We propose a novel Transformer-based module to address the data association
problem for multi-object tracking. From detections obtained by a pretrained
detector, this module uses only coordinates from bounding boxes to estimate an
affinity score between pairs of tracks extracted from two distinct temporal
windows. This module, named TWiX, is trained on sets of tracks with the
objective of discriminating pairs of tracks coming from the same object from
those which are not. Our module does not use the intersection over union
measure, nor does it requires any motion priors or any camera motion
compensation technique. By inserting TWiX within an online cascade matching
pipeline, our tracker C-TWiX achieves state-of-the-art performance on the
DanceTrack and KITTIMOT datasets, and gets competitive results on the MOT17
dataset. The code will be made available upon publication.

我们提出了一种新颖的基于 Transformer 的模块，用于解决多目标跟踪中的数据关联问题。该模块使用来自边界框的坐标来估计来自两个不同时间窗口的轨迹对之间的亲和度分数。通过将 TWiX 插入到在线级联匹配流水线中，我们的跟踪器 C-TWiX 在 DanceTrack 和 KITTIMOT 数据集上实现了最先进的性能，并在 MOT17 数据集上取得了竞争性的结果。

仅使用坐标进行多目标跟踪的学习数据关联

Learning Data Association for Multi-Object Tracking using Only  Coordinates

The remarkable success of deep neural networks (DNN) is often attributed to
their high expressive power and their ability to approximate functions of
arbitrary complexity. Indeed, DNNs are highly non-linear models, and activation
functions introduced into them are largely responsible for this. While many
works studied the expressive power of DNNs through the lens of their
approximation capabilities, quantifying the non-linearity of DNNs or of
individual activation functions remains an open problem. In this paper, we
propose the first theoretically sound solution to track non-linearity
propagation in deep neural networks with a specific focus on computer vision
applications. Our proposed affinity score allows us to gain insights into the
inner workings of a wide range of different architectures and learning
paradigms. We provide extensive experimental results that highlight the
practical utility of the proposed affinity score and its potential for
long-reaching applications.

该研究提出了一种理论上可靠的解决方案，用于跟踪计算机视觉应用中深度神经网络中的非线性传播，提供了详实的实验结果，突出了提出的亲和性评分的实用性及其潜在的广泛应用。