  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.
