  We present a deep learning-based object detection and object tracking
algorithm to study droplet motion in dense microfluidic emulsions. The deep
learning procedure is shown to correctly predict the droplets' shape and track
their motion at competitive rates as compared to standard clustering
algorithms, even in the presence of significant deformations. The deep learning
technique and tool developed in this work could be used for the general study
of the dynamics of biological agents in fluid systems, such as moving cells and
self-propelled micro organisms in complex biological flows.
