实时追踪实验室小鼠个体，解决密集饲养下的识别难题
Towards Continuous Home Cage Monitoring: An Evaluation of Tracking and Identification Strategies for Laboratory Mice
- 融合外观与运动信息的多目标追踪算法
- 基于变换器的识别模型实现30帧/秒精准编号
- 适用于不同品系与环境，减少识别切换错误
连续自动化监测实验小鼠可提升数据精度并改善动物福利。通过在笼内整合行为与生理监测，能更动态、临床相关地刻画疾病进展与治疗效果。然而，由于饲养密度高、外形相似、活动频繁及交互频繁，个体识别极具挑战。为此，我们开发了一套实时识别算法，利用定制耳标，在摄像头监控的数字笼中对佩戴耳标的鼠只进行精准身份标注。该流程包括三部分：(1) 结合外观与运动线索的自定义多目标追踪器（MouseTracks）；(2) 基于变换器的识别分类器（Mouseformer）；(3) 用于分配最终身份预测的轨迹关联线性规划模块（MouseMap）。系统以30帧每秒的速度持续运行，实现全天候笼内覆盖。相比现有方法，本方案在不同品系与多种环境因素下显著提升了追踪效率并降低了身份切换次数。
原文摘要 · Abstract (English)
Continuous, automated monitoring of laboratory mice enables more accurate data collection and improves animal welfare through real-time insights. Researchers can achieve a more dynamic and clinically relevant characterization of disease progression and therapeutic effects by integrating behavioral and physiological monitoring in the home cage. However, providing individual mouse metrics is difficult because of their housing density, similar appearances, high mobility, and frequent interactions. To address these challenges, we develop a real-time identification (ID) algorithm that accurately assigns ID predictions to mice wearing custom ear tags in digital home cages monitored by cameras. Our pipeline consists of three parts: (1) a custom multiple object tracker (MouseTracks) that combines appearance and motion cues from mice; (2) a transformer-based ID classifier (Mouseformer); and (3) a tracklet associator linear program to assign final ID predictions to tracklets (MouseMap). Our models assign an animal ID based on custom ear tags at 30 frames per second with 24/7 cage coverage. We show that our custom tracking and ID pipeline improves tracking efficiency and lowers ID switches across mouse strains and various environmental factors compared to current mouse tracking methods.
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