DARTH: Holistic Test-time Adaptation for Multiple Object Tracking

• URL: http://arxiv.org/abs/2310.01926v1
• Date: Tue, 3 Oct 2023 10:10:42 GMT
• Title: DARTH: Holistic Test-time Adaptation for Multiple Object Tracking
• Authors: Mattia Segu, Bernt Schiele, Fisher Yu
• Abstract summary: Multiple object tracking (MOT) is a fundamental component of perception systems for autonomous driving. 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. We introduce DARTH, a holistic test-time adaptation framework for MOT.
• Score: 87.72019733473562
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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