MVTD: A Benchmark Dataset for Maritime Visual Object Tracking

TL;DR

The paper introduces MVTD, a specialized maritime visual object tracking dataset with diverse scenarios, to evaluate and improve tracking algorithms in challenging maritime conditions.

Contribution

It provides the first comprehensive maritime VOT dataset with 182 high-resolution videos, enabling targeted evaluation and domain-specific improvements.

Findings

State-of-the-art algorithms perform poorly on MVTD without fine-tuning.

Fine-tuning on MVTD significantly improves tracking performance.

The dataset highlights the need for domain adaptation in maritime tracking.

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 "https://github.com/AhsanBaidar/MVTD".