Multi-Object Tracking by Iteratively Associating Detections with Uniform Appearance for Trawl-Based Fishing Bycatch Monitoring

TL;DR

This paper introduces a novel multi-object tracking method tailored for real-time fish monitoring in trawl-based fishing, enhancing tracking accuracy for targets with uniform appearance without increasing latency.

Contribution

The paper presents an iterative association step that improves tracking of uniform-appearance targets and can be integrated into existing tracking algorithms.

Findings

Outperforms state-of-the-art methods on underwater fish datasets.

Maintains real-time performance with no increase in latency.

Achieves higher HOTA, MOTA, and IDF1 metrics.

Abstract

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.