身份门控无人机手势控制的部署研究
A Deployment Study of Identity-Gated Drone Gesture Control
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中文总结 AI 辅助
本文提出身份门控手势控制栈IGate，结合面部验证与手势识别，在DJI Tello EDU上验证，离线等错误率0.32%，飞行中19.3%，手势分类准确率优于几何规则。
中文摘要 AI 辅助
基于视觉的手势控制会接受相机视野中任何手的命令，这在共享室内空间中是不安全的。本文提出了IGate，一个身份门控控制栈，包括手势控制和面部跟踪，其中仅当已注册的操作员通过验证时才允许执行命令。系统从20个初始面部帧进行少样本用户注册，无需事先针对用户的训练：验证通过余弦相似度将当前面部裁剪的嵌入与注册模板进行比较，而面部跟踪使用比例校正。手势控制通过使用在自定义数据集上训练的RBF-SVM分类提取的手部关键点来实现。此外，一个分层有限状态机处理模式选择、默认和回退行为。该方法在DJI Tello EDU上测试，每个组件在离线及飞行中进行了270次试验（其中149次飞行）评估。面部验证在离线时达到0.32%的等错误率，而飞行中为19.3%。在悬停锁定条件下，RBF-SVM手势分类器优于几何规则（准确率0.850对0.651），其中82%的差距来自深度通道。所有日志和复现脚本将发布。
英文摘要
Vision-based gesture control accepts commands from any hand in the camera field of view, which is unsafe in shared indoor spaces. This paper presents IGate, an identity-gated control stack that includes gesture control and face tracking, in which commands are admitted only when an enrolled operator is verified. The system performs few-shot user enrolment from 20 initial face frames, without prior user-specific training: verification compares an embedding of the current face crop against the enrolled template by cosine similarity, while face tracking uses proportional correction. Gesture control is achieved by classifying extracted hand landmarks using an RBF-SVM trained on a custom dataset. Additionally, a hierarchical finite-state machine handles mode selection, default, and fallback behaviours. The approach is tested on a DJI Tello EDU, each component evaluated offline and in-flight across 270 trials (149 flown). Face verification yields a 0.32% offline equal error rate versus 19.3% in-flight. Under hover-locked conditions, the RBF-SVM gesture classifier outperforms the geometric rule (0.850 vs. 0.651 accuracy), with 82% of this gap stemming from the depth channel. All logs and reproduction scripts will be released.
发表机构
- Université Paris-Saclay（巴黎-萨克雷大学）
- IBISC Laboratory（IBISC实验室）
机构由 AI 辅助整理，请以论文原文为准。
