Towards Continuous Home Cage Monitoring: An Evaluation of Tracking and Identification Strategies for Laboratory Mice

2025-07-10

AI Summary

In high-density group-housed laboratory mice, visual similarity and frequent social interactions impede reliable long-term individual identification, hindering precise association of behavioral and physiological data over time. Method: We propose a fully automated in-cage monitoring system integrating custom-designed ear tags, the multi-object tracking framework MouseTracks, a Transformer-based identity classification model (Mouseformer), and a trajectory optimization algorithm (MouseMap), enabling real-time (30 fps) individual identification and stable long-term trajectory association. Contribution/Results: Compared to existing methods, our system significantly reduces identity-switching errors across multiple mouse strains and housing conditions, substantially improving tracking robustness and accuracy. To our knowledge, this is the first system to achieve millisecond-precision continuous individual identity resolution in high-density group-housing settings—providing a reliable data foundation for disease progression modeling, pharmacological efficacy assessment, and animal welfare monitoring.

Technology Category

• Computer Vision: Motion & Tracking
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Multi-Robot Systems

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Accurate individual mouse tracking in dense housing conditions
• Real-time identification of mice with similar appearances and high mobility
• Integration of behavioral and physiological monitoring for disease progression

Innovation

• Real-time ID algorithm for mice tracking
• Custom multiple object tracker with motion cues
• Transformer-based ID classifier for accurate identification