Scaling Video Analytics on Constrained Edge Nodes
Scaling Video Analytics on Constrained Edge Nodes
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发表时间：
2019-05
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通讯作者：
Christopher Canel;Thomas Kim;Giulio Zhou;Conglong Li;Hyeontaek Lim;D. Andersen;M. Kaminsky;Subramanya R. Dulloor
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作者：
Christopher Canel;Thomas Kim;Giulio Zhou;Conglong Li;Hyeontaek Lim;D. Andersen;M. Kaminsky;Subramanya R. Dulloor
随着摄像机部署的不断增长，处理大量实时数据的需求给广域网基础设施带来了压力。当每个摄像头的带宽有限时，对于交通监控和行人跟踪等应用来说，将高质量视频流卸载到数据中心是不可行的。本文介绍了FilterForward，这是一种新的边缘到云系统，通过安装仅回程相关视频帧的轻型边缘过滤器，使基于数据中心的应用程序能够处理来自数千台摄像机的内容。FilterForward引入了快速且富有表现力的每个应用程序微分类器，它们共享计算以同时检测计算受限边缘节点上的数十个事件。只有匹配的事件才会被传输到云端。对两个真实世界摄像机馈给数据集的评估表明，FilterForward减少了一个数量级的带宽使用，同时提高了具有挑战性视频内容的计算效率和事件检测精度。
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.