论文标题
Top-K深视频分析：一种概率方法
Top-K Deep Video Analytics: A Probabilistic Approach
论文作者
论文摘要
深度神经网络（DNNS）的令人印象深刻的准确性对视频数据的实用分析提出了巨大要求。尽管有效且准确，但最新的视频分析系统并未支持除了选择和聚合查询之外的分析。在数据分析中，TOP-K是一个非常重要的分析操作，使分析师能够专注于最重要的实体。在本文中，我们介绍了珠穆朗玛峰，这是第一个支持高效且准确的TOP-K视频分析的系统。珠穆朗玛峰从具有概率保证的视频中排名并确定最有趣的框架/时刻。珠穆朗玛峰是一种构建的系统，该系统仔细合成了深度计算机视觉模型，不确定的数据管理和Top-K查询处理。对现实世界视频的评估和最新的视觉道路基准显示，珠穆朗玛峰的效率比基线方法高14.3倍至20.6倍，其结果准确性很高
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
