Robust Multi-Object Tracking in Dense Swarms with Query Propagation and Adaptive Attention

The query propagation paradigm provides a unified theoretical framework for end-to-end multi-object tracking, yet it still faces challenges in complex scenarios involving multi-scale variations, dense interactions, and trajectory fragmentation, including insufficient query initialization quality, imprecise feature alignment, and difficult identity recovery. Building upon MOTRv2, this paper proposes three core improvements. First, we design a geometric prior injection strategy based on sine–cosine encoding, which explicitly encodes target location and scale information into detection queries, providing high-quality initialization for tracking queries. Second, we propose a width–height-modulated deformable attention mechanism that dynamically adjusts the sampling range of deformable convolution according to target size, enabling fine-grained feature matching for multi-scale targets. Third, we construct a motion-direction-consistency-based trajectory re-association module that leverages motion continuity to efficiently recover lost trajectories without introducing additional appearance models. Furthermore, we introduce a progressive joint training strategy that optimizes detection and tracking modules in stages, effectively mitigating gradient competition in multi-task learning. Extensive quantitative and qualitative experiments on the BEE24, UAVSwarm, and VTMOT infrared datasets validate the effectiveness of the proposed method. On the UAVSwarm dataset, our method achieves state-of-the-art performance with 52.4% HOTA, 72.1% MOTA, and only 51 identity switches. Ablation studies further reveal the synergistic enhancement mechanism among the proposed modules.