DARTH: Holistic Test-time Adaptation for Multiple Object Tracking
In: 2023 IEEE/CVF International Conference on Computer Vision (ICCV, 2023
Online
Konferenz
Zugriff:
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. Project page: https://www.vis.xyz/pub/darth.
Titel: |
DARTH: Holistic Test-time Adaptation for Multiple Object Tracking
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Autor/in / Beteiligte Person: | Segu, Mattia ; 000-0002-9107-531X, id_orcid:0 ; Schiele, Bernt ; Yu, Fisher |
Link: | |
Zeitschrift: | 2023 IEEE/CVF International Conference on Computer Vision (ICCV, 2023 |
Veröffentlichung: | IEEE, 2023 |
Medientyp: | Konferenz |
ISBN: | 979-8-3503-0718-4 (print) |
DOI: | 10.3929/ethz-b-000651657 |
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