VU: Edge Computing-Enabled Video Usefulness Detection and its Application in Large-Scale Video Surveillance Systems

摘要

In the era of smart and connected communities, video surveillance systems, which typically involve tens to thousands of cameras, have increasingly become prominent components for public safety. In current practice, when a failure occurs in a video surveillance system, the operation and maintenance teams usually spend a substantial amount of time locating and identifying the failure; hence, the fast online response cannot be guaranteed in a large-scale video surveillance system. Meanwhile, the video data that contains potential failures consumes bandwidth that could be used for useful video data. The useless video will waste the scarce bandwidth in the network and storage usage in the cloud. The emergence of edge computing is highly promising in video preprocessing with an edge camera. A video surveillance system is a killer application for edge computing. In this article, we propose an edge computing-enabled video usefulness (i.e., VU) model for large-scale video surveillance systems. We also explore its application, e.g., early failure detection and bandwidth improvement. According to the usefulness of the video data, the VU model can locate a failure and send it to end-users on the fly. In this article, our goals are threefold: 1) proposing a comprehensive VU model. To the best of our knowledge, this is the first work to explore the feasibility of the VU model and to determine VU values in a real application; 2) reducing the mean time to detection (i.e., MTTD) efficiently via edge computing-enabled fast online failure detection approaches; and 3) relieving the network bandwidth for large-scale video surveillance systems. Our experimental results demonstrate the approaches in VU model accurately detect failures that were collected from a video surveillance system with approximately 4000 cameras. The MTTD is substantially shortened by the fast online detection approaches. The video data with the worst VU values is mostly discarded to lessen overload of the network.