SiamDiff: A Diffusion-Driven Siamese Network for Scale-Aware Anti-UAV Tracking

Unmanned aerial vehicle (UAV) tracking faces significant challenges due to small targets and background interference. Traditional anchor-based tracking algorithms require designing numerous proposals to capture such tiny targets, which entails unacceptable computational overhead. On the other hand, anchor-free tracking methods struggle to adapt to target scale variations, resulting in suboptimal tracking accuracy in anti-UAV tracking scenarios. To address these limitations, we pioneer the integration of diffusion models into visual tracking, proposing SiamDiff—a scale-adaptive anti-UAV framework. We reformulate the tracking task as a bounding box prediction problem, where a diffusion model is leveraged to generate scale-adaptive proposals. Furthermore, we propose a Learnable Mask Module (LMM) and a Frequency Channel Fusion Module (FCFM) to enhance discriminative feature extraction for small targets. Additionally, we design a Scale-Aware Diffusion Strategy (SADA) to boost robustness to scale variations. Experimental results on the Anti-UAV and Anti-UAV410 benchmarks demonstrate the effectiveness of our approach, achieving a State Accuracy (SA) of 71.90% and 67.03%, respectively, outperforming the baseline and other trackers. Moreover, our method shows superior adaptability to scale variations, confirming its robustness in complex anti-UAV tracking scenarios.