Tracking microrobots is challenging, considering their minute size and high speed. As the field progresses towards developing microrobots for biomedical applications and conducting mechanistic studies in physiologically relevant media (e.g., collagen), this challenge is exacerbated by the dense surrounding environments with feature size and shape comparable to microrobots. Herein, we report Motion Enhanced Multi-level Tracker (MEMTrack), a robust pipeline for detecting and tracking microrobots using synthetic motion features, deep learning-based object detection, and a modified Simple Online and Real-time Tracking (SORT) algorithm with interpolation for tracking. Our object detection approach combines different models based on the object's motion pattern. We trained and validated our model using bacterial micro-motors in collagen (tissue phantom) and tested it in collagen and aqueous media. We demonstrate that MEMTrack accurately tracks even the most challenging bacteria missed by skilled human annotators, achieving precision and recall of 77% and 48% in collagen and 94% and 35% in liquid media, respectively. Moreover, we show that MEMTrack can quantify average bacteria speed with no statistically significant difference from the laboriously-produced manual tracking data. MEMTrack represents a significant contribution to microrobot localization and tracking, and opens the potential for vision-based deep learning approaches to microrobot control in dense and low-contrast settings. All source code for training and testing MEMTrack and reproducing the results of the paper have been made publicly available https://github.com/sawhney-medha/MEMTrack.

利用合成运动特征、基于深度学习的目标检测以及修改了的Simple Online and Real-time Tracking (SORT)算法的等差插值，我们提出了用于检测和追踪微型机器人的Motion Enhanced Multi-level Tracker (MEMTrack)。我们的模型在胶原和水介质中对细菌微型马达进行了训练、验证和测试，证明了MEMTrack的准确性和可行性，为微型机器人定位和追踪以及在浓密和低对比度环境中的深度学习方法控制提供了新的可能性。

MEMTRACK: 基于深度学习的微型机器人在密集低对比度环境中的跟踪方法