  The widespread use of smart computer vision systems in our personal spaces
has led to an increased consciousness about the privacy and security risks that
these systems pose. On the one hand, we want these systems to assist in our
daily lives by understanding their surroundings, but on the other hand, we want
them to do so without capturing any sensitive information. Towards this
direction, this paper proposes a simple, yet robust privacy-preserving encoder
called BDQ for the task of privacy-preserving human action recognition that is
composed of three modules: Blur, Difference, and Quantization. First, the input
scene is passed to the Blur module to smoothen the edges. This is followed by
the Difference module to apply a pixel-wise intensity subtraction between
consecutive frames to highlight motion features and suppress obvious high-level
privacy attributes. Finally, the Quantization module is applied to the motion
difference frames to remove the low-level privacy attributes. The BDQ
parameters are optimized in an end-to-end fashion via adversarial training such
that it learns to allow action recognition attributes while inhibiting privacy
attributes. Our experiments on three benchmark datasets show that the proposed
encoder design can achieve state-of-the-art trade-off when compared with
previous works. Furthermore, we show that the trade-off achieved is at par with
the DVS sensor-based event cameras. Code available at:
https://github.com/suakaw/BDQ_PrivacyAR.
