Scaling Video Analytics on Constrained Edge Nodes

Part of Proceedings of Machine Learning and Systems 1 (MLSys 2019)

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Christopher Canel, Thomas Kim, Giulio Zhou, Conglong Li, Hyeontaek Lim, David G Andersen, Michael Kaminsky, Subramanya Dulloor


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 datacenter. Evaluation on two real-world camera feed datasets shows that FilterForward improves computational efficiency and event detection accuracy for challenging video content while substantially reducing network bandwidth use.