面向大规模集会规模空中人群监测的验证型适应：部署协议、严重程度定律与无标签无人机人群计数诊断，面向2034年沙特阿拉伯FIFA世界杯
Validated Adaptation for Aerial Crowd Monitoring at Mass Gathering Scale: A Deployment Protocol, a Severity Law, and a Diagnostic for Label-Free Drone Crowd Counting, Toward the FIFA World Cup 2034 (Saudi Arabia)
AI总结：
针对2034沙特世界杯空中人群监测，该研究提出无标签无人机计数的验证型适应方法，建立严重程度定律等，修复密集场景计数不足，完成受控实验与全分辨率语料库测试，形成部署协议。
AI中文摘要：
沙特阿拉伯将主办2034年FIFA世界杯，且已开展朝觐规模的人群管理工作。基于无人机的计数系统必须在无标签的情况下，对与训练语料库完全不同的画面保持准确性，且必须在踩踏形成前预警危险的人流涌入。我们基于525次受控运行、全分辨率语料库研究、五次证伪消融实验及五条件安全联锁评估，提供了经过验证的解决方案。无标签适应在四种损坏类型和五个严重程度等级下，可恢复31%-49%的偏移诱导误差，最强方法相比冻结源模型获得41.8的MAE（95%置信区间[34.1,49.6]，p=7.5×10^-10，d=2.52）。我们建立了将方法与恒定绝对边际、边际增长的方法区分开的严重程度定律，以及识别何种配置可安全飞行的稳定性预算。在承载真实+48 MAE空中差距的全分辨率语料库上（源模型重新训练后验证MAE为14.6，提升34%），适应修复了密集场景的计数不足，否则会低估正在形成的踩踏，且基于通量的风险模块在6个全长片段中的2个内对真实拥堵事件发出警报。我们定位了可恢复的误差：在旨在偏向物理信息守恒先验的机制（300帧片段，200ms间隔，比标准宽五倍）中，适应信号由归一化驱动而非流驱动；连续性残差对域偏移产生的比例计数误差不变，经四个开/关消融实验证实，相关系数r=0.999，且40%输入损坏仅使精度变动0.05 MAE。无标签偏移门显示偏移幅度与精度损伤呈秩独立（斯皮尔曼rho=0.20；真实偏移中rho=-0.60），量化了幅度门放弃的58%余量。我们确立了带尾部监测的无条件适应作为策略，最后给出六点协议。
英文摘要：
Saudi Arabia will host the 2034 FIFA World Cup and already operates crowd management at Hajj scale. Drone-based counting must hold accuracy on footage unlike anything in its training corpus, without labels, and must warn of dangerous inflow before a crush forms. We deliver a validated answer built on 525 controlled runs, a full-resolution corpus study, five falsification ablations, and a five-condition safety-interlock evaluation. Label-free adaptation recovers 31-49% of shift-induced error across four corruptions and five severities, with the strongest method gaining 41.8 MAE over the frozen source (95% CI [34.1, 49.6], p=7.5x10^-10, d=2.52). We establish a severity law separating methods with a constant absolute margin from the one whose margin grows, and a stability budget identifying which configuration is safe to fly. On a full-resolution corpus carrying a genuine +48 MAE aerial gap (source retrained to 14.6 validation MAE, a 34% improvement), adaptation repairs the dense-scene undercounting that would otherwise under-report a forming crush, and the flux-based risk module fires on real congestion episodes in 2 of 6 full-length clips. We localise the recoverable error: in a regime built to favor a physics-informed conservation prior (300-frame clips at 200ms spacing, five times wider than standard), the adaptation signal is normalisation-driven, not flow-driven; the continuity residual is invariant to the proportional counting errors domain shift produces, confirmed by four on/off ablations correlated at r=0.999 and a 40% input corruption moving accuracy by only 0.05 MAE. A label-free shift gate shows shift magnitude and accuracy damage are rank-independent (Spearman rho=0.20; rho=-0.60 among genuine shifts), quantifying the 58% of headroom a magnitude gate forgoes. We establish unconditional adaptation with tail monitoring as policy, closing with a six-point protocol.
