Motion-Driven Multi-Object Tracking of Model Organisms in Space Science Experiments

2026-04-29

AI Summary

This study addresses the challenge of long-term multi-animal tracking in microgravity space science experiments, where targets exhibit weak appearance cues, poor imaging conditions, complex motion patterns, and frequent interactions. To tackle this problem, the authors propose ART-Track, the first motion-centric multi-object tracking framework tailored to this scenario. ART-Track integrates multi-model motion estimation, motion-state-guided data association, and an uncertainty-aware adaptive fusion of spatial and motion cues to achieve highly robust trajectory reconstruction. The accompanying SpaceAnimal-MOT dataset and the proposed method significantly reduce identity switches on zebrafish and fruit fly sequences, maintaining strong identity consistency even under severe occlusion, deformation, and high-density interactions, thereby providing a reliable foundation for quantitative behavioral analysis.

Abstract

Automated animal behavior analysis relies on long-term, interpretable individual trajectories; however, multi-animal tracking in space science experimental videos remains highly challenging due to weak appearance cues, low-quality imaging, complex maneuvering behaviors, and frequent interactions. To address this problem, we first construct the SpaceAnimal-MOT dataset to characterize the motion complexity and long-term identity preservation challenges in biological videos acquired under microgravity conditions. We then propose ART-Track (Adaptive Robust Tracking), a motion-driven tracking framework tailored to this setting. Specifically, multi-model motion estimation is introduced to handle abrupt maneuvers and nonlinear motion, motion-state-driven association is designed to reduce identity switches under dense interactions and temporary mismatch, and uncertainty-adaptive fusion is used to dynamically balance spatial and motion cues when prediction reliability varies. Experimental results show that ART-Track significantly reduces identity switches on zebrafish and fruitfly sequences, while maintaining more stable association under occlusion, deformation, and high-density interactions, thereby providing a more reliable tracking foundation for downstream quantitative behavior analysis. The code is publicly available at https://github.com/yyy7777777/ART_TRACK/tree/main.

Problem

• multi-object tracking
• space science experiments
• model organisms
• motion complexity
• identity preservation

Innovation

• motion-driven tracking
• multi-model motion estimation
• motion-state-driven association
• uncertainty-adaptive fusion
• SpaceAnimal-MOT