<p>The perception of Unmanned Aerial Vehicles (UAVs), particularly in infrared videos, is crucial for effective Anti-UAV tasks. Object tracking, specifically in infrared videos, plays a potential role in this topic. Though numerous trackers have achieved great progress in Anti-UAV tasks, we find that capturing rapidly changing scenario information for the tracking compensation is crucial, which gets mere attention in the community. Therefore, we propose a Framework Strengthened by scenario compensation for Anti-UAV Tracking. Firstly, a collector is designed with temporal cues for getting the latent variation of the scenario during tracking. Secondly, a Local Tracking Attention Module(LTAM) is designed to inject scenario compensation into the local tracking process. Thirdly, a Global Detection Attention Module(GDAM) is designed to inject scenario compensation into the global detection process. Notably, the Tracking Framework we use is based on an off-the-shelf Tracker but possesses some optimizing structure and deployment. Finally, the Framework Strengthened by scenario compensation achieves robust and efficient UAV tracking in infrared scenarios. Extensive experiments show that our Tracking Framework with low latency outperforms previous state-of-the-art methods on four Anti-UAV benchmarks, shedding light on the effectiveness of our method.</p>