  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.

Computer Vision and Pattern Recognition;Machine Learning;Performance;Image and Video Processing;Machine Learning