Abstract
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 "this https URL.
Abstract (translated)
视觉对象跟踪（VOT）是一项具有广泛应用的基础任务，包括自主导航、监控和海事机器人技术。尽管通用目标追踪方面取得了重大进展，但海洋环境仍面临着独特的挑战，例如镜面水反射、低对比度目标、动态变化的背景以及频繁遮挡现象。这些复杂性显著降低了现有跟踪算法的性能，凸显了领域特定数据集的需求。 为了解决这一缺口，我们引入了海事视觉追踪数据集（MVTD），这是一个专为海洋VOT设计的全面且公开可用的基准测试库。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：船只、帆船和无人水面舰艇（USV）。该数据集捕捉了多样化的操作条件和海洋场景，反映了海事环境中实际存在的复杂性。 我们在MVTD基准测试上评估了14种最新的SOTA跟踪算法，发现与它们在通用数据集上的表现相比，性能显著下降。然而，在使用MVTD进行微调后，这些模型表现出明显的性能提升，这强调了领域适应的有效性和在专业追踪场景中转移学习的重要性。 MVTD数据集通过提供现实且具有挑战性的基准测试填补了视觉跟踪社区中的关键空白，为海洋场景的应用提供了支持。该数据集和源代码可在此链接处访问："this https URL"。
URL
https://arxiv.org/abs/2506.02866
