  Driven by advances in computer vision and the falling costs of camera
hardware, organizations are deploying video cameras en masse for the spatial
monitoring of their physical premises. Scaling video analytics to massive
camera deployments, however, presents a new and mounting challenge, as compute
cost grows proportionally to the number of camera feeds. This paper is driven
by a simple question: can we scale video analytics in such a way that cost
grows sublinearly, or even remains constant, as we deploy more cameras, while
inference accuracy remains stable, or even improves. We believe the answer is
yes. Our key observation is that video feeds from wide-area camera deployments
demonstrate significant content correlations (e.g. to other geographically
proximate feeds), both in space and over time. These spatio-temporal
correlations can be harnessed to dramatically reduce the size of the inference
search space, decreasing both workload and false positive rates in multi-camera
video analytics. By discussing use-cases and technical challenges, we propose a
roadmap for scaling video analytics to large camera networks, and outline a
plan for its realization.
