Concerns for the privacy of individuals captured in public imagery have led
to privacy-preserving action recognition. Existing approaches often suffer from
issues arising through obfuscation being applied globally and a lack of
interpretability. Global obfuscation hides privacy sensitive regions, but also
contextual regions important for action recognition. Lack of interpretability
erodes trust in these new technologies. We highlight the limitations of current
paradigms and propose a solution: Human selected privacy templates that yield
interpretability by design, an obfuscation scheme that selectively hides
attributes and also induces temporal consistency, which is important in action
recognition. Our approach is architecture agnostic and directly modifies input
imagery, while existing approaches generally require architecture training. Our
approach offers more flexibility, as no retraining is required, and outperforms
alternatives on three widely used datasets.

我们提出了一种通过人工选择隐私模板的方案来解决行为识别中的隐私保护问题，并利用一种有选择性隐藏属性且引入时间一致性的模糊化方法，该方法不需要重新训练，并在三个广泛使用的数据集上表现出较高的灵活性和性能。