ShipsMOT: A Comprehensive Benchmark and Framework for Multiobject Tracking of Ships

Abstract

Multiobject tracking of ships is crucial for various applications, such as maritime security and the development of ship autopilot systems. However, existing ship visual datasets primarily focus on ship detection tasks, lacking a fully open-source dataset for multiobject tracking research. Furthermore, current methods often struggle with extracting appearance features under complex sea conditions, varying scales and different ship types, affecting tracking precision. To address these issues, we propose ShipsMOT, a new benchmark dataset containing 121 video sequences with an average of 15.45 s per sequence, covering 15 distinct ship types and a total of 237,999 annotated bounding boxes. Additionally, we propose JDR-CSTrack, a ship multiobject tracking framework that improves feature extraction at different scales by optimising a joint detection and Re-ID network. JDR-CSTrack utilises the fusion of appearance and motion features for multilevel data association, thereby minimising track loss and ID switches. Experimental results confirm that ShipsMOT can serve as a benchmark for future research in ship multiobject tracking and validate the superiority of the proposed JDR-CSTrack framework. The dataset and code can be found on https://github.com/jpj0916/ShipsMOT.