Abstract
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.
Abstract (translated)
本文对刘和Szirányi提出的姿态识别方法进行了方法论分析，特别关注其评估协议的有效性。我们展示了所报告的近乎完美的准确率指标是由帧级别的随机训练-测试拆分导致的结果，这种拆分不可避免地将同一主体的数据混入了两个数据集之中，从而造成了严重的信息泄露问题。通过审查已发布的混淆矩阵、学习曲线以及数据集构建情况，我们证明该评估无法衡量对未见过个体的泛化能力。我们的研究结果强调，在基于视觉的姿态识别研究中进行独立于主体的数据拆分的重要性，尤其是在如无人机与人交互这样的应用领域，这些应用需要可靠地识别人群中新出现的手势。
URL
https://arxiv.org/abs/2602.17854
