指出无人机救援手势识别评估存在数据泄露问题，导致结果虚高。
On the Evaluation Protocol of Gesture Recognition for UAV-based Rescue Operation based on Deep Learning: A Subject-Independence Perspective
- 采用帧级随机划分数据集，导致同一人样本混入训练与测试集。
- 实际无法评估对未见人员手势的泛化能力，准确率不可信。
- 强调应使用跨主体划分数据，适合无人机交互等真实场景研究者。
本文针对刘与西拉尼提出的基于深度学习的无人机救援手势识别方法，重点分析其评估协议的有效性。研究表明，报告的近似完美准确率源于帧级随机划分训练测试集，不可避免地将同一受试者的样本同时分配到训练和测试集中，造成严重数据泄露。通过分析已发表的混淆矩阵、学习曲线及数据集构建方式，我们证实该评估未能衡量模型对未见个体的泛化能力。研究强调，在视觉手势识别研究中，尤其是涉及无人机-人类交互等应用场景时，必须采用主体无关的数据划分策略以确保评估可靠性。
原文摘要 · Abstract (English)
This paper presents a methodological analysis of the gesture-recognition approach proposed by Liu and Szirányi, with a particular focus on the validity of their evaluation protocol. We show that the reported near-perfect accuracy metrics result from a frame-level random train-test split that inevitably mixes samples from the same subjects across both sets, causing severe data leakage. By examining the published confusion matrix, learning curves, and dataset construction, we demonstrate that the evaluation does not measure generalization to unseen individuals. Our findings underscore the importance of subject-independent data partitioning in vision-based gesture-recognition research, especially for applications - such as UAV-human interaction - that require reliable recognition of gestures performed by previously unseen people.
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