Multiple object tracking (MOT) is a fundamental component of perception systems for autonomous driving, and its robustness to unseen conditions is a requirement to avoid life-critical failures. Despite the urge of safety in driving systems, no solution to the MOT adaptation problem to domain shift in test-time conditions has ever been proposed. However, the nature of a MOT system is manifold - requiring object detection and instance association - and adapting all its components is non-trivial. In this paper, we analyze the effect of domain shift on appearance-based trackers, and introduce DARTH, a holistic test-time adaptation framework for MOT. We propose a detection consistency formulation to adapt object detection in a self-supervised fashion, while adapting the instance appearance representations via our novel patch contrastive loss. We evaluate our method on a variety of domain shifts - including sim-to-real, outdoor-to-indoor, indoor-to-outdoor - and substantially improve the source model performance on all metrics. Code: https://github.com/mattiasegu/darth.

翻译：多目标跟踪（MOT）是自动驾驶感知系统的基本组成部分，其对未见过场景的鲁棒性是避免致命性故障的必要条件。尽管驾驶系统对安全性有迫切需求，但目前尚未有解决MOT系统在测试时条件下面临域偏移自适应问题的方案。然而，MOT系统的本质具有多源性——需要目标检测与实例关联——且对其所有组件进行自适应调整并非易事。本文分析了域偏移对基于外观的跟踪器的影响，并提出了DARTH——一种面向MOT的整体式测试时自适应框架。我们提出检测一致性公式以自监督方式实现目标检测自适应，同时通过新颖的斑块对比损失对实例外观表征进行自适应。我们在多种域偏移场景下（包括仿真到真实、室外到室内、室内到室外）评估了该方法，并在所有指标上显著提升了源模型的性能。代码：https://github.com/mattiasegu/darth。