Scaling Video Analytics on Constrained Edge Nodes
This addresses bandwidth and computational constraints for applications like traffic monitoring and pedestrian tracking on edge nodes, representing a domain-specific incremental improvement.
The paper tackles the problem of processing large volumes of real-time video data from cameras with limited bandwidth by presenting FilterForward, an edge-to-cloud system that reduces bandwidth use by an order of magnitude while improving computational efficiency and event detection accuracy.
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.