Abstract
As low-cost surveillance cameras grow rapidly, we advocate for these cameras to be zero streaming: ingesting videos directly to their local storage and only communicating with the cloud in response to queries. To support queries over videos stored on zero-streaming cameras, we describe a system that spans the cloud and cameras. The system builds on two unconventional ideas. When ingesting video frames, a camera learns accurate knowledge on a sparse sample of frames, rather than learning inaccurate knowledge on all frames; in executing one query, a camera processes frames in multiple passes with multiple operators trained and picked by the cloud during the query, rather than one-pass processing with operator(s) decided ahead of the query. On diverse queries over 750-hour videos and with typical wireless network bandwidth and low-cost camera hardware, our system runs at more than 100x video realtime. It outperforms competitive alternative designs by at least 4x and up to two orders of magnitude.
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URL
http://arxiv.org/abs/1904.12342