PhyTracker: An Online Tracker for Phytoplankton
Yang Yu, Qingxuan Lv, Yuezun Li, Zhiqiang Wei, Junyu Dong

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,…
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Taxonomy
TopicsEnvironmental Monitoring and Data Management · Microbial Community Ecology and Physiology · Scientific Computing and Data Management
