MANTA: Physics-Informed Generalized Underwater Object Tracking

TL;DR

MANTA is a physics-informed underwater object tracking framework that uses contrastive learning and a multi-stage pipeline to improve robustness and accuracy across various water conditions, outperforming existing methods.

Contribution

The paper introduces MANTA, a novel underwater tracking method combining physics-informed contrastive learning with a multi-stage association pipeline for enhanced robustness.

Findings

Achieves state-of-the-art performance on four underwater benchmarks.

Improves Success AUC by up to 6 percent over previous methods.

Ensures stable long-term tracking under diverse underwater conditions.

Abstract

Underwater object tracking is challenging due to wavelength dependent attenuation and scattering, which severely distort appearance across depths and water conditions. Existing trackers trained on terrestrial data fail to generalize to these physics-driven degradations. We present MANTA, a physics-informed framework integrating representation learning with tracking design for underwater scenarios. We propose a dual-positive contrastive learning strategy coupling temporal consistency with Beer-Lambert augmentations to yield features robust to both temporal and underwater distortions. We further introduce a multi-stage pipeline augmenting motion-based tracking with a physics-informed secondary association algorithm that integrates geometric consistency and appearance similarity for re-identification under occlusion and drift. To complement standard IoU metrics, we propose Center-Scale Consistency (CSC) and Geometric Alignment Score (GAS) to assess geometric fidelity. Experiments on four underwater benchmarks (WebUOT-1M, UOT32, UTB180, UWCOT220) show that MANTA achieves state-of-the-art performance, improving Success AUC by up to 6 percent, while ensuring stable long-term generalized underwater tracking and efficient runtime.