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Parallel Fish School Tracking Based on Multiple Appearance Feature Detection.

Zhitao Wang1,2, Chunlei Xia2, Jangmyung Lee1

  • 1Department of Electronics Engineering, Pusan National University, Busan 46241, Korea.

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|June 2, 2021
PubMed
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This study presents a new method for tracking zebrafish schools using multiple features for accurate movement trajectory analysis. The approach achieves high accuracy and real-time performance, outperforming deep learning methods without requiring expensive hardware.

Area of Science:

  • Animal Behavior
  • Computer Vision
  • Biotechnology

Background:

  • Zebrafish are crucial model organisms in biological research.
  • Vision-based monitoring of zebrafish behavior is challenging due to their appearance, movement, and occlusions.
  • Accurate tracking of individual fish within a school is essential for behavioral analysis.

Purpose of the Study:

  • To develop an accurate and efficient method for tracking multiple zebrafish in a school.
  • To overcome challenges in vision-based monitoring, including occlusions and similar appearances.
  • To enable real-time trajectory data acquisition for zebrafish behavior studies.

Main Methods:

  • A multiple appearance feature-based fish detection scheme using shape index features of fish heads and bodies.
Keywords:
Kalman filterSORTclusteringshape indexzebrafish

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  • A parallel tracking scheme based on the SORT framework, integrating multiple fish features and motion states.
  • Evaluation using seven video clips under diverse conditions (scale, arena size, frame rate, resolution).

    • Main Results:

    • The method successfully tracked up to 100 individual zebrafish.
    • Achieved high correct tracking ratios (98.60%–99.86%) and correct identification ratios (97.73%–100%).
    • Demonstrated superiority over advanced deep learning-based methods in performance.

    Conclusions:

    • The proposed parallel fish school tracking method provides accurate and stable trajectories for large numbers of zebrafish.
    • The method offers real-time tracking capabilities suitable for online data acquisition.
    • It achieves high accuracy without the need for high-cost hardware, making it accessible for various research settings.