PhyTracker: An Online Tracker for Phytoplankton

TL;DR

PhyTracker is an innovative in situ tracking framework that uses advanced modules to automatically monitor phytoplankton, overcoming challenges like mobility constraints and impurities, validated by extensive experiments.

Contribution

The paper introduces PhyTracker, a novel tracking framework with three modules specifically designed for phytoplankton, improving accuracy over traditional methods.

Findings

Outperforms conventional tracking methods on PMOT dataset

Demonstrates general applicability on MOT dataset

Effectively differentiates phytoplankton from impurities

Abstract

Phytoplankton, a crucial component of aquatic ecosystems, requires efficient monitoring to understand marine ecological processes and environmental conditions. Traditional phytoplankton monitoring methods, relying on non-in situ observations, are time-consuming and resource-intensive, limiting timely analysis. To address these limitations, we introduce PhyTracker, an intelligent in situ tracking framework designed for automatic tracking of phytoplankton. PhyTracker overcomes significant challenges unique to phytoplankton monitoring, such as constrained mobility within water flow, inconspicuous appearance, and the presence of impurities. Our method incorporates three innovative modules: a Texture-enhanced Feature Extraction (TFE) module, an Attention-enhanced Temporal Association (ATA) module, and a Flow-agnostic Movement Refinement (FMR) module. These modules enhance feature capture, differentiate between phytoplankton and impurities, and refine movement characteristics, respectively. Extensive experiments on the PMOT dataset validate the superiority of PhyTracker in phytoplankton tracking, and additional tests on the MOT dataset demonstrate its general applicability, outperforming conventional tracking methods. This work highlights key differences between phytoplankton and traditional objects, offering an effective solution for phytoplankton monitoring.