  The state-of-the-art deep learning-based object recognition YOLO algorithm
and object tracking DeepSORT algorithm are combined to analyze digital images
from fluid dynamic simulations of multi-core emulsions and soft flowing
crystals and to track moving droplets within these complex flows. The YOLO
network was trained to recognize the droplets with synthetically prepared data,
thereby bypassing the labor-intensive data acquisition process. In both
applications, the trained YOLO + DeepSORT procedure performs with high accuracy
on the real data from the fluid simulations, with low error levels in the
inferred trajectories of the droplets and independently computed ground truth.
Moreover, using commonly used desktop GPUs, the developed application is
capable of analyzing data at speeds that exceed the typical image acquisition
rates of digital cameras (30 fps), opening the interesting prospect of
realizing a low-cost and practical tool to study systems with many moving
objects, mostly but not exclusively, biological ones. Besides its practical
applications, the procedure presented here marks the first step towards the
automatic extraction of effective equations of motion of many-body soft flowing
systems.
