  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.
