Multi-Ship Tracking by Robust Similarity metric
Hongyu Zhao Gongming Wei Yang Xiao Xianglei Xing
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.