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Robust Real-Time Sperm Tracking with Identity Reassignment Using Extended Kalman Filtering.

Mahdieh Gol Hassani¹, Mozafar Saadat¹, Peiran Lei¹

¹Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK.

Sensors (Basel, Switzerland)

|December 31, 2025

Summary

This summary is machine-generated.

This study introduces an enhanced sperm tracking system using an Extended Kalman Filter (EKF) with BoT-SORT. The new method significantly improves sperm identity continuity in microscopy, crucial for fertility diagnostics and Intracytoplasmic Sperm Injection (ICSI).

Keywords:

Extended Kalman Filter computer vision in assisted reproductive technology identity reassignment microscopy video analysis multi-object tracking sperm tracking

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Area of Science:

Biomedical Engineering
Computer Vision
Reproductive Biology

Background:

Accurate sperm tracking is vital for automated Intracytoplasmic Sperm Injection (ICSI) and fertility diagnostics.
Challenges include identity fragmentation, overcounting, and tracking instability in crowded, low-contrast microscopy.
Maintaining correct sperm identities across video frames is crucial for reliable sperm selection.

Purpose of the Study:

To develop a robust two-layer tracking framework to enhance sperm identity continuity.
To improve the reliability of sperm tracking in challenging microscopy conditions.
To integrate Extended Kalman Filter (EKF) with BoT-SORT for superior tracking performance.

Main Methods:

A two-layer tracking framework integrating BoT-SORT with an Extended Kalman Filter (EKF).
EKF models sperm trajectories using position, velocity, and heading for motion prediction and ID correction.
Evaluation on the VISEM dataset using standard multi-object tracking (MOT) metrics and trajectory statistics.

Main Results:

The EKF-BoT-SORT framework improved IDF1 score from 80.30% to 84.84% compared to BoT-SORT.
Reduced ID switches from 176 to 132 and decreased ID overcount from 68.75% to 37.5%.
Increased average track duration from 74.4 to 91.3 frames, demonstrating enhanced identity preservation.

Conclusions:

The EKF layer significantly enhances sperm identity preservation in tracking systems.
The proposed method maintains real-time feasibility, crucial for clinical applications.
Offers a practical foundation for integrating computer vision into ICSI workflows and sperm motility analysis.