MANTA通过物理模型增强，实现水下目标稳定跟踪
MANTA: Physics-Informed Generalized Underwater Object Tracking
- 融合光照衰减物理模型与对比学习，提升特征鲁棒性
- 多阶段追踪框架使长时跟踪成功率提升6个百分点
- 适合需要水下长期追踪的科研与海洋应用
水下目标跟踪因波长依赖的衰减和散射而困难，导致不同深度和水质下的外观严重失真。现有基于陆地数据训练的追踪器无法泛化到此类物理退化场景。我们提出MANTA，一个将表征学习与追踪设计结合的物理感知框架。采用时空一致性与Beer-Lambert增强的双重正样本对比学习策略，生成对时间和水下畸变均鲁棒的特征。进一步设计多阶段流程，将运动追踪与基于几何一致性与外观相似性的物理感知二次关联算法结合，解决遮挡与漂移下的重识别问题。为补充标准IoU指标，提出中心-尺度一致性（CSC）与几何对齐得分（GAS）评估几何保真度。在四个水下基准（WebUOT-1M、UOT32、UTB180、UWCOT220）上的实验表明，MANTA达到当前最优性能，成功AUC最高提升6%，并保证长期泛化追踪稳定性与高效运行时间。
原文摘要 · Abstract (English)
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.
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