Real-Time Robust Single Sperm Tracking via Adaptive Particle Filtering
Fengling Meng, Yinran Chen, Xióngbiāo Luó · 2022
Assisted reproductive technology is commonly used to treat infertility. Motility-based selection of high-quality sperms is the key to improve the successful rate of artificial assisted reproduction. Visually tracking the sperms on optical microscopic video frames is essential to evaluate their motility before the selection. Unfortunately, current methods easily fail to precisely track the sperms in real time. This work is to accurately and robustly detect and track single sperm based on microscopic video frames. We propose a modified background subtraction method to detect multiple sperms in successive frames. We also introduce an adaptive particle filtering method to accurately and robustly track the trajectory of a single sperm in real time. Specifically, this method models the sperm movement by comparing its histogram information at different positions on microscopic images and uses adaptive particle filtering to approximate the optimal state of the sperm. The experimental results demonstrate that our method can achieve much better tracking accuracy than other visual tracking methods, providing more reliable sperm motility analysis. In particular, our method can successfully re-track the same sperm when it appears again on the microscopic focal plane after disappearing in a few frames, while the other compared tracking methods usually fail to re-track the same sperm after its back.