  The strong temporal consistency of surveillance video enables compelling
compression performance with traditional methods, but downstream vision
applications operate on decoded image frames with a high data rate. Since it is
not straightforward for applications to extract information on temporal
redundancy from the compressed video representations, we propose a novel system
which conveys temporal redundancy within a sparse decompressed representation.
We leverage a video representation framework called ADDER to transcode framed
videos to sparse, asynchronous intensity samples. We introduce mechanisms for
content adaptation, lossy compression, and asynchronous forms of classical
vision algorithms. We evaluate our system on the VIRAT surveillance video
dataset, and we show a median 43.7% speed improvement in FAST feature detection
compared to OpenCV. We run the same algorithm as OpenCV, but only process
pixels that receive new asynchronous events, rather than process every pixel in
an image frame. Our work paves the way for upcoming neuromorphic sensors and is
amenable to future applications with spiking neural networks.
