针对热红外无人机群追踪，提出轻量级实时跟踪方法。
Edge-Aware Thermal Infrared UAV Swarm Tracking
- 用自适应运动建模增强卡尔曼滤波，提升动态追踪能力。
- 在BSB基准上实现高轨迹连续性与低延迟计算的平衡。
- 适合边缘设备部署，尤其适用于复杂视觉环境下的实时追踪。
热红外（TIR）成像对视觉退化环境下无人机群作业至关重要。然而，由于外观线索有限、频繁遮挡和快速机动，追踪小型无人机仍具挑战。尽管反无人机挑战赛等基准推动了显著进展，现有方法多侧重精度而忽视边缘设备的实时计算约束。标准卡尔曼滤波（KF）虽满足实时性要求，但其恒定速度假设常因高度动态飞行与热成像抖动失效。更复杂的非线性估计算法可提升鲁棒性，却往往带来额外计算开销。为此，我们提出基于自适应运动卡尔曼滤波（AKKF）的边缘感知在线追踪流程，在保持实时效率的同时引入状态相关运动建模。结合瞬时误报抑制与运动驱动预测滞留机制，该方法在复杂TIR条件下显著提升轨迹连续性。在超越强基线（BSB）基准上的实验首次联合评估了追踪性能与计算效率，为未来实时部署提供了参考。
原文摘要 · Abstract (English)
Thermal infrared (TIR) imaging is essential for UAV swarm operations in visually degraded environments. However, tracking tiny UAVs remains challenging due to limited appearance cues, frequent occlusions, and rapid maneuvers. Despite significant progress driven by benchmarks such as the Anti-UAV challenge, existing methods primarily prioritize accuracy while overlooking the computational constraints of real-time edge deployment. The standard Kalman Filter (KF) offers the efficiency required for edge devices, yet its constant-velocity assumption often breaks down under highly dynamic UAV motion and thermal sensor jitter. More sophisticated nonlinear estimators can improve robustness but often introduce additional computational costs. To address this gap, we propose an edge-aware online tracking pipeline centered on the Adaptive Kinematic Kalman Filter (AKKF), which augments the linear KF with state-dependent kinematic modeling while preserving real-time efficiency. Combined with transient false-positive suppression and kinematics-driven predictive coasting, the presented pipeline improves trajectory continuity under challenging TIR conditions. Experiments on the Beyond Strong Baseline (BSB) benchmark provide a starting point for edge-aware UAV tracking by jointly evaluating tracking performance and computational efficiency, offering insights toward future real-time deployment.
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