  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.
