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

Accurate and real-time sperm tracking is essential for automation in Intracytoplasmic Sperm Injection (ICSI) and fertility diagnostics, where maintaining correct identities across frames improves the reliability of sperm selection. However, identity fragmentation, overcounting, and tracking instability remain persistent challenges in crowded and low-contrast microscopy conditions. This study presents a robust two-layer tracking framework that integrates BoT-SORT with an Extended Kalman Filter (EKF) to enhance identity continuity. The EKF models sperm trajectories using a nonlinear state that includes position, velocity, and heading, allowing it to predict motion across occlusions and correct fragmented or duplicate IDs. We evaluated the framework on microscopy videos from the VISEM dataset using standard multi-object tracking (MOT) metrics and trajectory statistics. Compared to BoT-SORT, the proposed EKF-BoT-SORT achieved notable improvements: IDF1 increased from 80.30% to 84.84%, ID switches reduced from 176 to 132, average track duration extended from 74.4 to 91.3 frames, and ID overcount decreased from 68.75% to 37.5%. These results confirm that the EKF layer significantly improves identity preservation without compromising real-time feasibility. The method may offer a practical foundation for integrating computer vision into ICSI workflows and sperm motility analysis systems.