The aim of in-trawl catch monitoring for use in fishing operations is to
detect, track and classify fish targets in real-time from video footage.
Information gathered could be used to release unwanted bycatch in real-time.
However, traditional multi-object tracking (MOT) methods have limitations, as
they are developed for tracking vehicles or pedestrians with linear motions and
diverse appearances, which are different from the scenarios such as livestock
monitoring. Therefore, we propose a novel MOT method, built upon an existing
observation-centric tracking algorithm, by adopting a new iterative association
step to significantly boost the performance of tracking targets with a uniform
appearance. The iterative association module is designed as an extendable
component that can be merged into most existing tracking methods. Our method
offers improved performance in tracking targets with uniform appearance and
outperforms state-of-the-art techniques on our underwater fish datasets as well
as the MOT17 dataset, without increasing latency nor sacrificing accuracy as
measured by HOTA, MOTA, and IDF1 performance metrics.

该论文提出了一种基于现有观测中心跟踪算法的新型多目标跟踪方法，采用新的迭代关联步骤大大提高了对具有均匀外观的目标的跟踪性能。