  We present a passive non-line-of-sight method that infers the number of
people or activity of a person from the observation of a blank wall in an
unknown room. Our technique analyzes complex imperceptible changes in indirect
illumination in a video of the wall to reveal a signal that is correlated with
motion in the hidden part of a scene. We use this signal to classify between
zero, one, or two moving people, or the activity of a person in the hidden
scene. We train two convolutional neural networks using data collected from 20
different scenes, and achieve an accuracy of $\approx94\%$ for both tasks in
unseen test environments and real-time online settings. Unlike other passive
non-line-of-sight methods, the technique does not rely on known occluders or
controllable light sources, and generalizes to unknown rooms with no
re-calibration. We analyze the generalization and robustness of our method with
both real and synthetic data, and study the effect of the scene parameters on
the signal quality.
