通过合成阴影数据提升手部清洗识别模型在真实场景下的鲁棒性
Shadow Augmentation for Handwashing Action Recognition: from Synthetic to Real Datasets
- 用合成数据测试不同阴影属性对模型性能的影响
- 发现更重更大阴影能更好缓解环境变化导致的性能下降
- 提出适用于真实数据的阴影增强方法，跨模型和数据集有效
面向户外部署的视频分析系统易受阴影等环境变化影响，现有研究显示阴影引发的分布偏移会显著降低系统性能。本文以提升食品安全为目标，研究手部清洗动作识别中阴影带来的模型崩溃点的缓解策略。通过合成数据探索训练时应包含的最佳阴影属性，实验表明更重、更大的阴影更具效果。基于此，提出一种可应用于真实数据的阴影增强方法。结果表明该方法在模型训练中有效，且在不同神经网络架构和数据集间具有一致性。
原文摘要 · Abstract (English)
Video analytics systems designed for deployment in outdoor conditions can be vulnerable to many environmental changes, particularly changes in shadow. Existing works have shown that shadow and its introduced distribution shift can cause system performance to degrade sharply. In this paper, we explore mitigation strategies to shadow-induced breakdown points of an action recognition system, using the specific application of handwashing action recognition for improving food safety. Using synthetic data, we explore the optimal shadow attributes to be included when training an action recognition system in order to improve performance under different shadow conditions. Experimental results indicate that heavier and larger shadow is more effective at mitigating the breakdown points. Building upon this observation, we propose a shadow augmentation method to be applied to real-world data. Results demonstrate the effectiveness of the shadow augmentation method for model training and consistency of its effectiveness across different neural network architectures and datasets.
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