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.

通过将 TIoU 指标纳入现有的多对象跟踪框架，如 DeepSort 和 ByteTrack，我们持续提升了这些框架的跟踪性能。

基于健壮相似性度量的多船追踪

Multi-Ship Tracking by Robust Similarity metric

Evaluation protocols play key role in the developmental progress of text
detection methods. There are strict requirements to ensure that the evaluation
methods are fair, objective and reasonable. However, existing metrics exhibit
some obvious drawbacks: 1) They are not goal-oriented; 2) they cannot recognize
the tightness of detection methods; 3) existing one-to-many and many-to-one
solutions involve inherent loopholes and deficiencies. Therefore, this paper
proposes a novel evaluation protocol called Tightness-aware
Intersect-over-Union (TIoU) metric that could quantify completeness of ground
truth, compactness of detection, and tightness of matching degree.
Specifically, instead of merely using the IoU value, two common detection
behaviors are properly considered; meanwhile, directly using the score of TIoU
to recognize the tightness. In addition, we further propose a straightforward
method to address the annotation granularity issue, which can fairly evaluate
word and text-line detections simultaneously. By adopting the detection results
from published methods and general object detection frameworks, comprehensive
experiments on ICDAR 2013 and ICDAR 2015 datasets are conducted to compare
recent metrics and the proposed TIoU metric. The comparison demonstrated some
promising new prospects, e.g., determining the methods and frameworks for which
the detection is tighter and more beneficial to recognize. Our method is
extremely simple; however, the novelty is none other than the proposed metric
can utilize simplest but reasonable improvements to lead to many interesting
and insightful prospects and solving most the issues of the previous metrics.
The code is publicly available at this https URL .

本文提出了一种新的评估协议，称为紧密感知交集联合（TIoU）度量，旨在解决现有度量的一些明显缺陷，并提供了一种简单有效的解决方案来同时识别文本线和单词检测，并通过实验表明他们的方法更紧密、更有利于识别，代码公开在此 https URL。