探究手部姿态与阴影对手部清洗动作识别的影响
Exploring the Impact of Hand Pose and Shadow on Hand-washing Action Recognition
AI总结：
该研究针对洗手动作识别场景，探究手部姿态与阴影对分类器性能的影响，定义了性能崩溃点，发现阴影越重越大崩溃越早、姿态大变时准确率近零，并提出用适度偏离的非标准姿态数据缓解姿态引发的崩溃。
摘要：
在现实世界中，基于摄像头的应用系统会面临诸多挑战，包括环境因素与分布偏移。本文以洗手动作识别这一具体应用场景，研究姿态与阴影如何影响分类器的性能。为此，我们生成了带有目标变化的合成数据，以引入可控的分布偏移。基于该合成数据集，我们将分类器的崩溃点定义为系统性能开始急剧下降的临界点，并证实这些点受姿态与阴影条件的显著影响。具体而言，更浓重、范围更大的阴影会导致崩溃点提前出现。此外，一个有趣的发现是，当姿态变化幅度较大时，模型准确率会降至几乎为零。另外，我们针对姿态引发的崩溃点提出了一种简单的缓解策略：利用来自非标准姿态的额外训练数据。结果表明，额外训练姿态的最优选择是与标准姿态存在适度偏差、旋转角度在50-60度的姿态。
英文摘要：
In the real world, camera-based application systems can face many challenges, including environmental factors and distribution shift. In this paper, we investigate how pose and shadow impact a classifier's performance, using the specific application of handwashing action recognition. To accomplish this, we generate synthetic data with desired variations to introduce controlled distribution shift. Using our synthetic dataset, we define a classifier's breakdown points to be where the system's performance starts to degrade sharply, and we show these are heavily impacted by pose and shadow conditions. In particular, heavier and larger shadows create earlier breakdown points. Also, it is intriguing to observe model accuracy drop to almost zero with bigger changes in pose. Moreover, we propose a simple mitigation strategy for pose-induced breakdown points by utilizing additional training data from non-canonical poses. Results show that the optimal choices of additional training poses are those with moderate deviations from the canonical poses with 50-60 degrees of rotation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
