  Many critical applications rely on cameras to capture video footage for
analytical purposes. This has led to concerns about these cameras accidentally
capturing more information than is necessary. In this paper, we propose a deep
learning approach towards protecting privacy in camera-based systems. Instead
of specifying specific objects (e.g. faces) are privacy sensitive, our
technique distinguishes between salient (visually prominent) and non-salient
objects based on the intuition that the latter is unlikely to be needed by the
application.
