In this work, we study self-supervised multiple object tracking without using
any video-level association labels. We propose to cast the problem of multiple
object tracking as learning the frame-wise associations between detections in
consecutive frames. To this end, we propose differentiable soft object
assignment for object association, making it possible to learn features
tailored to object association with differentiable end-to-end training. With
this training approach in hand, we develop an appearance-based model for
learning instance-aware object features used to construct a cost matrix based
on the pairwise distances between the object features. We train our model using
temporal and multi-view data, where we obtain association pseudo-labels using
optical flow and disparity information. Unlike most self-supervised tracking
methods that rely on pretext tasks for learning the feature correspondences,
our method is directly optimized for cross-object association in complex
scenarios. As such, the proposed method offers a reidentification-based MOT
approach that is robust to training hyperparameters and does not suffer from
local minima, which are a challenge in self-supervised methods. We evaluate our
proposed model on the KITTI, Waymo, nuScenes, and Argoverse datasets,
consistently improving over other unsupervised methods ($7.8\%$ improvement in
association accuracy on nuScenes).

本文旨在研究不使用任何视频级联标签的自监督多目标跟踪问题，并通过不同 iable 可微分的软目标分配来解决问题，使用时间和多视图数据进行训练，从而获得关于目标的关联伪标签。与大多数依赖于学习特征对应关系预算任务的自监督跟踪方法不同，我们的方法直接优化于复杂情景下的交叉对象关联，为鲁棒的重新识别 MOT 方法提供了一种解决方案，不会受到 Minima 的影响。在 KITTI、Waymo、nuScenes 和 Argoverse 数据集上评估我们的模型，与其他无监督方法相比，一致提高了 nuScenes 上的关联精度（7.8%）。