针对微重力下生物体多目标追踪难题，提出自适应鲁棒追踪框架。
Motion-Driven Multi-Object Tracking of Model Organisms in Space Science Experiments
- 基于运动驱动设计多模型运动估计与状态感知关联机制。
- 在斑马鱼和果蝇视频中显著减少身份切换，提升遮挡下的追踪稳定性。
- 适合微重力生物学实验、长期行为分析等需要高可靠轨迹的场景。
自动化动物行为分析依赖于长期可解释的个体轨迹；然而，由于外观线索弱、成像质量低、运动复杂且频繁交互，空间科学实验视频中的多动物追踪仍极具挑战。为此，我们首先构建了SpaceAnimal-MOT数据集，刻画微重力环境下生物视频的运动复杂性与长期身份保持难题。随后提出ART-Track（自适应鲁棒追踪）框架，针对该场景优化：引入多模型运动估计以应对突发转向与非线性运动，设计运动状态驱动的关联策略以降低密集交互与临时错配导致的身份切换，采用不确定性自适应融合动态平衡空间与运动线索。实验表明，ART-Track在斑马鱼与果蝇序列上显著减少身份切换，同时在遮挡、形变及高密度交互下保持更稳定关联，为下游定量行为分析提供更可靠的追踪基础。代码已公开于https://github.com/yyy7777777/ART_TRACK/tree/main。
原文摘要 · Abstract (English)
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.
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