SCT-MOT: Enhancing Air-to-Air Multiple UAVs Tracking With Swarm-Coupled Motion and Trajectory Guidance

Air-to-air tracking of multiple uncrewed aerial vehicles (UAVs) in swarm scenarios presents significant challenges due to the complex nonlinear group movements and weak per-frame visual cues for small UAV objects, which often result in detection failures, trajectory fragmentation, and identity switches. Although existing methods have attempted to improve performance by incorporating trajectory prediction, they model each object independently, neglecting the swarm-level motion dependencies. Moreover, their limited integration between motion prediction and appearance representation weakens the spatio-temporal consistency required for tracking in visually ambiguous and cluttered environments, making it difficult to maintain coherent trajectories and reliable associations. To address these challenges, we propose SCT-MOT, a multiple UAVs tracking framework that integrates swarm-coupled motion modeling and trajectory-guided feature fusion. First, we develop a swarm motion-aware trajectory prediction (SMTP) module that jointly models historical trajectories and posture-aware appearance features from a swarm-level perspective, enabling more accurate forecasting of the nonlinear, coupled group trajectories. Second, we design a trajectory-guided spatio-temporal feature fusion module that aligns predicted positions with historical visual cues and deeply integrates them with current frame features, enhancing temporal consistency and spatial discriminability for weak objects. Extensive experiments on three public air-to-air swarm UAV tracking datasets, including AIRMOT, MOT-FLY, and UAVSwarm, demonstrate that SMTP achieves more accurate trajectory forecasts and yields a 1.21% identity F1 score improvement over the state-of-the-art (SOTA) trajectory prediction module EqMotion when integrated into the same MOT framework. Overall, our SCT-MOT consistently achieves superior accuracy and robustness compared to SOTA trackers across multiple metrics under complex swarm scenarios.