A Deep Learning Framework for Multi-Object Tracking in Space Animal Behavior Studies

In space environments, microgravity, high radiation, and weak magnetic fields induce behavioral alterations in animals, resulting in erratic movement patterns that complicate tracking. These challenges impede accurate behavioral analysis, especially in multi-object scenarios. To address this issue, this study proposes a deep learning-based multi-object tracking (MOT) framework specifically designed for space animals. The proposed method decouples appearance and motion features through dual-stream inputs and employs modality-specific encoders (MSEs), which are fused via a heterogeneous graph network to model cross-modal spatio-temporal relationships. Additionally, an object re-detection module is integrated to maintain identity continuity during occlusions or rapid movements. This approach is validated using public datasets of space-observed Drosophila and zebrafish, with experimental results demonstrating superior performance compared with existing tracking methods. This work highlights the potential of artificial intelligence as a valuable tool in behavioral studies, enabling reliable animal tracking and analysis under extreme space conditions and supporting future research in space life sciences.