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Updated: Jan 29, 2026

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Enhanced CenterTrack for Robust Underwater Multi-Fish Tracking.

Jinfeng Wang1, Mingrun Lin1, Zhipeng Cheng1

  • 1College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China.

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Summary
This summary is machine-generated.

This study introduces an improved CenterTrack framework for precise multi-fish tracking in challenging aquaculture environments. The method enhances fish identification and trajectory continuity, enabling better analysis of fish behavior.

Keywords:
MF25 datasetmulti-branch feature extractionmulti-object fish trackingocclusion handlingunderwater aquaculture

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Area of Science:

  • Aquaculture technology
  • Computer vision
  • Animal behavior analysis

Background:

  • Monitoring fish movement is crucial for understanding behavior and group dynamics in aquaculture.
  • Underwater tracking faces challenges like occlusions, similar appearances, and non-rigid motion.

Purpose of the Study:

  • To develop an improved CenterTrack framework for accurate multi-fish tracking in aquaculture.
  • To address challenges posed by dense populations, occlusions, and visual similarity.

Main Methods:

  • Implemented a multi-branch feature extractor for enhanced individual discrimination.
  • Utilized occlusion-aware output heads to estimate fish visibility states.
  • Employed a three-stage cascade association module for improved trajectory continuity.

Main Results:

  • Introduced the Multi-Fish 25 (MF25) dataset with 75 annotated fish.
  • Achieved high performance metrics: IDF1 of 82.5%, MOTA of 85.8%, and IDP of 84.7% on the MF25 dataset.
  • Demonstrated the framework's suitability for practical, online tracking.

Conclusions:

  • The proposed framework significantly improves multi-fish tracking accuracy in aquaculture.
  • High-quality trajectories generated provide a foundation for future behavioral studies.
  • The modular and efficient design supports real-world application in aquaculture settings.