  The impressive accuracy of deep neural networks (DNNs) has created great
demands on practical analytics over video data. Although efficient and
accurate, the latest video analytic systems have not supported analytics beyond
selection and aggregation queries. In data analytics, Top-K is a very important
analytical operation that enables analysts to focus on the most important
entities. In this paper, we present Everest, the first system that supports
efficient and accurate Top-K video analytics. Everest ranks and identifies the
most interesting frames/moments from videos with probabilistic guarantees.
Everest is a system built with a careful synthesis of deep computer vision
models, uncertain data management, and Top-K query processing. Evaluations on
real-world videos and the latest Visual Road benchmark show that Everest
achieves between 14.3x to 20.6x higher efficiency than baseline approaches with
high result accuracy
