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.

翻译：我们提出了一种基于深度学习的方法，用于在行走液滴和颗粒侵入体实验中追踪感兴趣的目标。在典型的行走液滴实验中，一种被称为“行走者”的液滴会在同种液体的振动浴自由表面上横向推进。这种运动是液滴与每次弹跳后自身产生的表面波相互作用的结果。行走液滴在其运动过程中可能表现出高度不规则的轨迹，包括快速加速以及与浴中其他行走液滴的复杂相互作用。与流体动力学实验类似，颗粒物质实验由非常小的固体颗粒振动浴和较大的固体“侵入体”组成。与流体液滴类似，侵入体因浴的波动与域相互作用并移动，但其运动速度通常比液滴慢得多且不光滑。当引入多个侵入体时，它们之间也会表现出复杂的相互作用。我们利用最先进的目标检测模型YOLO和匈牙利算法，实时精确提取行走者或侵入体的轨迹。所提出的方法能够从广泛实验设置下获取的数字图像中追踪单个或多个行走者/侵入体，且不会出现身份交换问题。因此，本工作中开发的深度学习方法可用于自动化、高效、快速且精确地提取行走液滴和颗粒流动实验中感兴趣的可观测量。这种提取能力对于下游任务（如构建数据驱动的粗粒度动力学模型以描述感兴趣目标的运动及相互作用）至关重要。