标题： MVTD：一个用于海上视觉目标跟踪的基准数据集 显示英文标题

标题： MVTD: A Benchmark Dataset for Maritime Visual Object Tracking

摘要： 视觉目标跟踪（VOT）是一项基础任务，在自主导航、监控和海洋机器人等领域有着广泛的应用。 尽管通用目标跟踪取得了显著进展，但海洋环境仍然带来了独特的挑战，包括镜面水反射、低对比度目标、动态变化的背景以及频繁的遮挡。 这些复杂性显著降低了最先进的跟踪算法的性能，凸显了领域特定数据集的需求。 为了解决这一差距，我们引入了海洋视觉跟踪数据集（MVTD），这是一个专门设计用于海洋VOT的综合且公开可用的基准。 MVTD 包含 182 个高分辨率视频序列，总计约 150,000 帧，并包括四个代表性目标类别：船、船、帆船和无人水面舰艇（USV）。 该数据集捕捉了各种操作条件和海洋场景，反映了海洋环境中真实世界的复杂性。 我们在 MVTD 基准上评估了 14 种最近的 SOTA 跟踪算法，并观察到与通用数据集相比性能大幅下降。 然而，当在 MVTD 上微调时，这些模型表现出显著的性能提升，强调了领域适应和迁移学习在专用跟踪上下文中的有效性。 MVTD 数据集通过为海洋场景提供现实且具有挑战性的基准，在视觉跟踪社区中填补了一个关键空白。 数据集和源代码可以在这里访问： "https://github.com/AhsanBaidar/MVTD"。 显示英文摘要

摘要： Visual Object Tracking (VOT) is a fundamental task with widespread applications in autonomous navigation, surveillance, and maritime robotics. Despite significant advances in generic object tracking, maritime environments continue to present unique challenges, including specular water reflections, low-contrast targets, dynamically changing backgrounds, and frequent occlusions. These complexities significantly degrade the performance of state-of-the-art tracking algorithms, highlighting the need for domain-specific datasets. To address this gap, we introduce the Maritime Visual Tracking Dataset (MVTD), a comprehensive and publicly available benchmark specifically designed for maritime VOT. MVTD comprises 182 high-resolution video sequences, totaling approximately 150,000 frames, and includes four representative object classes: boat, ship, sailboat, and unmanned surface vehicle (USV). The dataset captures a diverse range of operational conditions and maritime scenarios, reflecting the real-world complexities of maritime environments. We evaluated 14 recent SOTA tracking algorithms on the MVTD benchmark and observed substantial performance degradation compared to their performance on general-purpose datasets. However, when fine-tuned on MVTD, these models demonstrate significant performance gains, underscoring the effectiveness of domain adaptation and the importance of transfer learning in specialized tracking contexts. The MVTD dataset fills a critical gap in the visual tracking community by providing a realistic and challenging benchmark for maritime scenarios. Dataset and Source Code can be accessed here "https://github.com/AhsanBaidar/MVTD".