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.

翻译：水下目标跟踪因波长相关的衰减和散射效应而极具挑战性，这些效应会随深度和水体条件严重扭曲目标外观。现有基于陆地数据训练的跟踪器难以泛化至此类物理驱动的退化场景。本文提出MANTA，一种将表征学习与跟踪设计相融合的物理信息驱动框架，专为水下场景设计。我们提出一种双正例对比学习策略，将时序一致性与比尔-朗伯增强相结合，从而生成对时序变化和水下畸变均具有鲁棒性的特征。进一步，我们引入多阶段处理流程，在基于运动的跟踪基础上，通过物理信息驱动的二次关联算法进行增强，该算法整合几何一致性与外观相似性，以应对遮挡和漂移情况下的重识别任务。为补充传统的交并比指标，我们提出中心尺度一致性与几何对齐评分，用于评估几何保真度。在四个水下基准数据集（WebUOT-1M、UOT32、UTB180、UWCOT220）上的实验表明，MANTA实现了最先进的性能，将成功率曲线下面积提升最高达6%，同时确保稳定的长期广义水下跟踪能力与高效的运行效率。