  The risk of unauthorized remote access of streaming video from networked
cameras underlines the need for stronger privacy safeguards. We propose a
lens-free coded aperture camera system for human action recognition that is
privacy-preserving. While coded aperture systems exist, we believe ours is the
first system designed for action recognition without the need for image
restoration as an intermediate step. Action recognition is done using a deep
network that takes in as input, non-invertible motion features between pairs of
frames computed using phase correlation and log-polar transformation. Phase
correlation encodes translation while the log polar transformation encodes
in-plane rotation and scaling. We show that the translation features are
independent of the coded aperture design, as long as its spectral response
within the bandwidth has no zeros. Stacking motion features computed on frames
at multiple different strides in the video can improve accuracy. Preliminary
results on simulated data based on a subset of the UCF and NTU datasets are
promising. We also describe our prototype lens-free coded aperture camera
system, and results for real captured videos are mixed.
