  We present a deep-learning based tracking objects of interest in walking
droplet and granular intruder experiments. In a typical walking droplet
experiment, a liquid droplet, known as \textit{walker}, propels itself
laterally on the free surface of a vibrating bath of the same liquid. This
motion is the result of the interaction between the droplets and the surface
waves generated by the droplet itself after each successive bounce. A walker
can exhibit a highly irregular trajectory over the course of its motion,
including rapid acceleration and complex interactions with the other walkers
present in the same bath. In analogy with the hydrodynamic experiments, the
granular matter experiments consist of a vibrating bath of very small solid
particles and a larger solid \textit{intruder}. Like the fluid droplets, the
intruder interacts with and travels the domain due to the waves of the bath but
tends to move much slower and much less smoothly than the droplets. When
multiple intruders are introduced, they also exhibit complex interactions with
each other. We leverage the state-of-art object detection model YOLO and the
Hungarian Algorithm to accurately extract the trajectory of a walker or
intruder in real-time. Our proposed methodology is capable of tracking
individual walker(s) or intruder(s) in digital images acquired from a broad
spectrum of experimental settings and does not suffer from any identity-switch
issues. Thus, the deep learning approach developed in this work could be used
to automatize the efficient, fast and accurate extraction of observables of
interests in walking droplet and granular flow experiments. Such extraction
capabilities are critically enabling for downstream tasks such as building
data-driven dynamical models for the coarse-grained dynamics and interactions
of the objects of interest.
