感知到追捕：面向开放世界的无人机检测与自主追捕的以轨迹为中心的时序推理
Perception-to-Pursuit: Track-Centric Temporal Reasoning for Open-World Drone Detection and Autonomous Chasing
AI总结：
P2P通过轨迹为中心的时序推理框架，提升了无人机轨迹预测精度和追捕可行性，实现了准确的预测与可操作的追捕规划。
摘要：
自主无人机追捕不仅需要检测无人机，还需要以能够实现动力学可行拦截的方式预测其轨迹。现有跟踪方法优化预测准确性，但忽略了追捕可行性，导致99.9%的时间轨迹在物理上无法拦截。我们提出了感知到追捕（P2P），一种以轨迹为中心的时序推理框架，连接检测和可操作的追捕规划。我们的方法将无人机运动表示为紧凑的8维标记，捕捉速度、加速度、尺度和平滑度，使12帧因果Transformer能够推断未来行为。我们引入了拦截成功率（ISR）指标，以在现实拦截器约束下衡量追捕可行性。在Anti-UAV-RGBT数据集上评估，包含226个真实无人机序列，P2P实现了28.12像素平均位移误差和0.597 ISR，相对于仅跟踪的基线方法，轨迹预测精度提高了77%，追捕可行性提高了597倍，同时保持完美的无人机分类准确率（100%）。我们的工作证明了对运动模式的时序推理能够实现准确的预测和可操作的追捕规划。
英文摘要：
Autonomous drone pursuit requires not only detecting drones but also predicting their trajectories in a manner that enables kinematically feasible interception. Existing tracking methods optimize for prediction accuracy but ignore pursuit feasibility, resulting in trajectories that are physically impossible to intercept 99.9% of the time. We propose Perception-to-Pursuit (P2P), a track-centric temporal reasoning framework that bridges detection and actionable pursuit planning. Our method represents drone motion as compact 8-dimensional tokens capturing velocity, acceleration, scale, and smoothness, enabling a 12-frame causal transformer to reason about future behavior. We introduce the Intercept Success Rate (ISR) metric to measure pursuit feasibility under realistic interceptor constraints. Evaluated on the Anti-UAV-RGBT dataset with 226 real drone sequences, P2P achieves 28.12 pixel average displacement error and 0.597 ISR, representing a 77% improvement in trajectory prediction and 597x improvement in pursuit feasibility over tracking-only baselines, while maintaining perfect drone classification accuracy (100%). Our work demonstrates that temporal reasoning over motion patterns enables both accurate prediction and actionable pursuit planning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
