In recent years, sperm analysis has become increasingly important in the treatment of fertility issues. Traditionally, sperm quality was assessed by an expert through the use of a microscope to examine samples. CASA (Computer-Assisted Sperm Analysis) systems were developed1 to aid these experts in measuring factors that impact sperm quality such as semen volume, total number of sperm, concentration, vitality, motility or morphology2 with the aim of selecting optimal sperm for fertility treatments. Computer vision techniques are used to estimate these parameters by counting the number of individuals, checking the motility of each one and even classifying them by their morphology on a small portion of the sample. Recently, deep learning methods have been improving the performance of the computer vision tasks of detection, classification and tracking needed to perform the analysis.3,4 However, such methods often use models with a large number of parameters to achieve high levels of accuracy and precision. Some disadvantages of using big models are the need of high-end GPUs in both training and inference stages and long processing times. This drawbacks often turn image processing into the bottleneck of semen quality assessment. Lighter models are proving to be capable of real-time processing with good results.4 Our paper studies on the performance of a simple proposed tracking optimization method on different hardware, including a high-end server, a standard personal laptop and an embedded system with GPU. This work seeks to find a compromise between model accuracy and processing time, studying low parameter models that can be used in real-time scenarios. © 2023 SPIE.