Video Analytics with Zero-streaming Cameras
Video Analytics with Zero-streaming Cameras
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发表时间：
2019-04
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通讯作者：
Mengwei Xu;Tiantu Xu;Yunxin Liu;F. Lin
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作者：
Mengwei Xu;Tiantu Xu;Yunxin Liu;F. Lin
低成本相机可实现强大的分析。一个无法探索的机会是，大多数被捕获的视频保持“冷”而不会被查询。为了提高效率，我们主张这些相机为零流：将视频捕获到本地存储中，并仅在请求分析时与云进行通信。如何有效地查询零流相机？我们的响应是称为Diva的相机/云运行时系统。它解决了两个关键挑战：在视频捕获过程中最好地使用有限的相机资源；在查询执行过程中快速探索大量视频。 Diva贡献了两种非常规技术。 （1）捕获视频时，相机会构建稀疏而准确的地标框架，从中学习可靠的知识来加速未来的查询。 （2）执行查询时，摄像头会在多个通行证中处理越来越昂贵的操作员的帧。因此，Diva在整个查询的执行过程中呈现并不断完善不精确的查询结果。在超过15个视频中，总共持续720个小时的各种查询，Diva以100倍的视频实时运行，并且表现优于竞争性替代设计。据我们所知，Diva是第一个查询存储在低成本远程摄像机上的大型视频的系统。
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.