VU : video usefulness and its application in large-scale video surveillance systems
Hui Min Sun, Liang Xu, Weisong Shi · 2017
In the era of smart and connected communities, a video surveillance system, which usually involves tens and thousands of video cameras, has increasingly become a prominent component for the public safety. In the current practice, when the video surveillance system has a failure, the operation and maintenance team usually spends a lot of time to identify and locate the failure, which cannot guarantee real-time in a large-scale video surveillance system. Meanwhile, the video data with a failure wastes amount of storage space in the cloud. The emergence of edge computing is very promising in the preprocessing for source video data at an edge camera, and video surveillance systems are one of the popular applications for edge computing. In this paper, we propose VU, a Video Usefulness model for large-scale video surveillance systems, and explore its application, such as early failure detection and storage saving. The VU model evaluates the usefulness of video data in a real-time fashion and notifies failures to end-users on the fly.