Scaling Video Analytics Systems to Large Camera Deployments
This addresses the problem of high compute costs for organizations deploying massive camera networks, proposing a novel approach that could make large-scale video analytics more feasible.
The paper tackles the challenge of scaling video analytics to large camera deployments by aiming to reduce compute costs sublinearly or keep them constant while maintaining or improving inference accuracy, leveraging spatio-temporal correlations between camera feeds to decrease workload and false positive rates.
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.