  Persistent surveillance from camera networks, such as at parking lots, UAVs,
etc., often results in large amounts of video data, resulting in significant
challenges for inference in terms of storage, communication and computation.
Compressive cameras have emerged as a potential solution to deal with the data
deluge issues in such applications. However, inference tasks such as action
recognition require high quality features which implies reconstructing the
original video data. Much work in compressive sensing (CS) theory is geared
towards solving the reconstruction problem, where state-of-the-art methods are
computationally intensive and provide low-quality results at high compression
rates. Thus, reconstruction-free methods for inference are much desired. In
this paper, we propose reconstruction-free methods for action recognition from
compressive cameras at high compression ratios of 100 and above. Recognizing
actions directly from CS measurements requires features which are mostly
nonlinear and thus not easily applicable. This leads us to search for such
properties that are preserved in compressive measurements. To this end, we
propose the use of spatio-temporal smashed filters, which are compressive
domain versions of pixel-domain matched filters. We conduct experiments on
publicly available databases and show that one can obtain recognition rates
that are comparable to the oracle method in uncompressed setup, even for high
compression ratios.
