Scaling Video Analytics on Constrained Edge Nodes

TL;DR

FilterForward is an edge-to-cloud system that efficiently processes video streams by filtering relevant frames at the edge, significantly reducing bandwidth and improving event detection accuracy in constrained environments.

Contribution

It introduces lightweight microclassifiers for edge filtering and demonstrates substantial bandwidth reduction and enhanced detection accuracy in real-world scenarios.

Findings

Bandwidth use reduced by an order of magnitude

Improved event detection accuracy

Enhanced computational efficiency on edge nodes

Abstract

As video camera deployments continue to grow, the need to process large volumes of real-time data strains wide area network infrastructure. When per-camera bandwidth is limited, it is infeasible for applications such as traffic monitoring and pedestrian tracking to offload high-quality video streams to a datacenter. This paper presents FilterForward, a new edge-to-cloud system that enables datacenter-based applications to process content from thousands of cameras by installing lightweight edge filters that backhaul only relevant video frames. FilterForward introduces fast and expressive per-application microclassifiers that share computation to simultaneously detect dozens of events on computationally constrained edge nodes. Only matching events are transmitted to the cloud. Evaluation on two real-world camera feed datasets shows that FilterForward reduces bandwidth use by an order of magnitude while improving computational efficiency and event detection accuracy for challenging video content.