LMD-TShip<sup>⋆</sup>: Vision Based Large-Scale Maritime Ship Tracking Benchmark for Autonomous Navigation Applications
Yunxiao Shan, Shanghua Liu, Yunfei Zhang, Min Jing, Huawei Xu
Abstract
Accurate ship tracking is very important for the security of maritime activities, especially the raising requirements of autonomous navigation applications, e.g., autonomous surface vehicles (ASVs). Unlike deep-learning-based object-tracking methods are prevailing in autonomous driving because of good environmental robustness and high tracking accuracy, few deep-tracking models can be found for maritime ships. The main reason for that is the lack of qualified ship datasets, especially datasets with ship-based perspectives. Therefore, a large-scale, high-definition dataset for ship tracking, LMD-TShip (Large Maritime Dataset), is provided in this paper. In this dataset, five types of ships are included, from cargo ships, fishing ships, passenger ships, and speed boats to unmanned ships. Specifically, LMD-TShip consists of 40,240 frames in 191 videos, each of which is carefully and manually annotated with bounding boxes in YOLO format. Moreover, 13 attributes are used to label videos, e.g., scale variation (SV), occlusion (OCC), basically covering tracking challenges of maritime ship tracking. Next, a detailed analysis is carried out to demonstrate the characteristics of LMD-TShip. Finally, experiments with five baseline short-term tracking models on the dataset are performed, e.g., ECO, SiamRPN++, and the experimental results demonstrate its good evaluation ability, which will provide effective means for training and testing tracking models related to maritime ships.