trACT：时间揭示机载相机陷阱
trACT: temporal revelation Airborne Camera Trap
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- Johannes Kepler University Linz（林茨约翰内斯·开普勒大学）
- German Aerospace Center（德国航空航天中心）
- BOKU University（维也纳农业大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
trACT提出一种轻量级实时空中机器人框架，结合时间最大池化与自监督运动异常检测，实现无人机在复杂环境下对目标的自主精确验证。
中文摘要 AI 辅助
使用无人机进行有效的远程监控和监视常常受到严重的环境和热杂波、动态植被、目标伪装以及系统延迟的阻碍。受猛禽盘旋并稳定视线以隔离细微地面运动的狩猎策略启发，我们提出了trACT（时间揭示机载相机陷阱），一个专为自主消费级无人机设计的轻量级实时空中机器人框架。该系统集成了时间最大池化（TMP），一种低级信号处理方法，将滚动积分窗口内难以察觉的运动转化为稳健的值和时间编码，并结合自监督运动异常检测，以将目标运动与由阵风和无人机漂移引起的背景环境运动区分开来。为了克服机械和处理延迟，trACT将运动预测与自动云台稳定光学变焦验证以及公平的多目标验证平衡相结合。在茂密森林野生动物栖息地和监视场景中的广泛实地实验表明，trACT成功弥合了广域空中监控与在挑战性操作条件下精确自主目标验证之间的差距。
英文摘要
Effective remote monitoring and surveillance using drones are frequently impeded by severe environmental and thermal clutter, dynamic vegetation, target camouflage, and system latency. Drawing inspiration from the hunting strategies of birds of prey that hover and stabilize their vision to isolate subtle ground motion, we introduce trACT (temporal revelation Airborne Camera Trap), a lightweight, real-time aerial robotics framework designed for autonomous consumer drones. The system integrates Temporal Max Pooling (TMP), a low-level signal processing method that transforms imperceptible movement across a rolling integration window into robust value and time encodings, with self-supervised motion anomaly detection to isolate target motion from background environmental motion caused by wind gusts and drone drift. To overcome mechanical and processing delays, trACT combines motion prediction with automated gimbal-stabilized optical zoom verification and equitable multi-target verification balancing. Extensive real-world field experiments in densely forested wildlife habitats and surveillance scenarios demonstrate that trACT successfully bridges the gap between wide-area aerial monitoring and precise, autonomous target verification under challenging operational conditions.
