Abstract

Human sperm motility analysis is a key method in assessing male fertility. It was suggested that performance of automatic sperm motility analysis systems can be enhanced by adopting multi-target tracking algorithms developed originally for radar technology. We review and appraise several target tracking algorithms operating on synthetic and actual sperm images and compare their performance. Simulations and observations of images of real sperm cells suggest that the joint probability data association filter with track-coalescence-avoiding (JPDA*) outperforms other evaluated algorithms. This is also the result obtained on images of swimming tadpoles.

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