Multi-Ship Tracking by Robust Similarity metric

TL;DR

This paper introduces a robust similarity metric called TIoU that improves multi-ship tracking performance by addressing IoU limitations caused by wave turbulence and low frame rates, enhancing existing tracking frameworks.

Contribution

The paper proposes TIoU, a shape-aware similarity metric that enhances multi-ship tracking accuracy by overcoming IoU's limitations in turbulent maritime conditions.

Findings

TIoU improves tracking accuracy in ship datasets.

Integration of TIoU enhances DeepSort and ByteTrack frameworks.

Results show reduced identity switches and better robustness.

Abstract

Multi-ship tracking (MST) as a core technology has been proven to be applied to situational awareness at sea and the development of a navigational system for autonomous ships. Despite impressive tracking outcomes achieved by multi-object tracking (MOT) algorithms for pedestrian and vehicle datasets, these models and techniques exhibit poor performance when applied to ship datasets. Intersection of Union (IoU) is the most popular metric for computing similarity used in object tracking. The low frame rates and severe image shake caused by wave turbulence in ship datasets often result in minimal, or even zero, Intersection of Union (IoU) between the predicted and detected bounding boxes. This issue contributes to frequent identity switches of tracked objects, undermining the tracking performance. In this paper, we address the weaknesses of IoU by incorporating the smallest convex shapes that enclose both the predicted and detected bounding boxes. The calculation of the tracking version of IoU (TIoU) metric considers not only the size of the overlapping area between the detection bounding box and the prediction box, but also the similarity of their shapes. Through the integration of the TIoU into state-of-the-art object tracking frameworks, such as DeepSort and ByteTrack, we consistently achieve improvements in the tracking performance of these frameworks.