  Nowadays, computer-aided sperm analysis (CASA) systems have made a big leap
in extracting the characteristics of spermatozoa for studies or measuring human
fertility. The first step in sperm characteristics analysis is sperm detection
in the frames of the video sample. In this article, we used RetinaNet, a deep
fully convolutional neural network as the object detector. Sperms are small
objects with few attributes, that makes the detection more difficult in
high-density samples and especially when there are other particles in semen,
which could be like sperm heads. One of the main attributes of sperms is their
movement, but this attribute cannot be extracted when only one frame would be
fed to the network. To improve the performance of the sperm detection network,
we concatenated some consecutive frames to use as the input of the network.
With this method, the motility attribute has also been extracted, and then with
the help of the deep convolutional network, we have achieved high accuracy in
sperm detection. The second step is tracking the sperms, for extracting the
motility parameters that are essential for indicating fertility and other
studies on sperms. In the tracking phase, we modify the CSR-DCF algorithm. This
method also has shown excellent results in sperm tracking even in high-density
sperm samples, occlusions, sperm colliding, and when sperms exit from a frame
and re-enter in the next frames. The average precision of the detection phase
is 99.1%, and the F1 score of the tracking method evaluation is 96.61%. These
results can be a great help in studies investigating sperm behavior and
analyzing fertility possibility.
