Low-cost cameras enable powerful analytics. An unexploited opportunity is that most captured videos remain "cold" without being queried. For efficiency, we advocate for these cameras to be zero streaming: capturing videos to local storage and communicating with the cloud only when analytics is requested. How to query zero-streaming cameras efficiently? Our response is a camera/cloud runtime system called DIVA. It addresses two key challenges: to best use limited camera resource during video capture; to rapidly explore massive videos during query execution. DIVA contributes two unconventional techniques. (1) When capturing videos, a camera builds sparse yet accurate landmark frames, from which it learns reliable knowledge for accelerating future queries. (2) When executing a query, a camera processes frames in multiple passes with increasingly more expensive operators. As such, DIVA presents and keeps refining inexact query results throughout the query's execution. On diverse queries over 15 videos lasting 720 hours in total, DIVA runs at more than 100x video realtime and outperforms competitive alternative designs. To our knowledge, DIVA is the first system for querying large videos stored on low-cost remote cameras.
翻译:低成本相机能够进行强大的分析。 一个未开发的机会是大多数拍摄到的视频仍然“冷”而没有被询问。 为了效率,我们主张这些相机是零流的:只有在请求分析时,才捕捉视频到当地的存储和与云层通信; 如何有效地查询零流相机? 我们的反应是一个名为DIVA的相机/库德运行时间系统。 它解决了两个主要挑战: 在视频捕捉中最佳地使用有限的相机资源; 在询问执行过程中迅速探索大规模视频。 DIVA提供了两种非传统的技术。 (1) 摄像头在拍摄视频时,建立稀疏而准确的标志性框架,从中学习加快未来查询的可靠知识。 (2) 当执行查询时,一个与越来越昂贵的操作者进行多次传送的相机程序框架。 因此, DIVA在整个查询执行过程中展示并不断改进直径查询结果。 在超过15个总共720小时的视频中, DIVA运行于100多部视频实时和超常规的竞争性替代设计。 在我们的知识中, DIVA是第一个查询存储低成本远程摄像机的大型视频的系统。