Accurate and Robust Sperm Tracking via Adaptive Marginalized Particle Filtering

Fengling Meng, Yinran Chen, Xiongbiao Luo

Abstract: Human fertility continues deteriorating globally in recent decades. Artificial assisted reproductive technology is an effective solution to infertility treatment. Using computer-aided semen analysis systems to visually analyze the motility of sperms and select high-quality targets is widely concerned. Selecting motile sperm requires accurate and robust tracking of the individual target in the microscopic videos. Unfortunately, existing methods may fail to track the sperms in real time, especially for some motile sperms that swim out of the focal plane for a few frames and then swim back, exhibiting temporary disappearance and subsequent reappearance. In this letter, we propose an adaptive color histogram-based marginalized particle filter to accurately and robustly track sperm in real time. The experimental results on both synthetic and clinical microscopic videos demonstrated that the proposed method achieves higher accuracy compared to the alternative methods. Particularly, our method can successfully track the motile sperms with complex movements, showing higher robustness than other methods.