DoveDB: A Declarative and Low-Latency Video Database

IF 2.6 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI:10.14778/3611540.3611582

Ziyang Xiao, Dongxiang Zhang, Zepeng Li, Sai Wu, Kian-Lee Tan, Gang Chen

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引用次数: 0

Abstract

Concerning the usability and efficiency to manage video data generated from large-scale cameras, we demonstrate DoveDB, a declarative and low-latency video database. We devise a more comprehensive video query language called VMQL to improve the expressiveness of previous SQL-like languages, which are augmented with functionalities for model-oriented management and deployment. We also propose a light-weight ingestion scheme to extract tracklets of all the moving objects and build semantic indexes to facilitate efficient query processing. For user interaction, we construct a simulation environment with 120 cameras deployed in a road network and demonstrate three interesting scenarios. Using VMQL, users are allowed to 1) train a visual model using SQL-like statement and deploy it on dozens of target cameras simultaneously for online inference; 2) submit multi-object tracking (MOT) requests on target cameras, store the ingested results and build semantic indexes; and 3) issue an aggregation or top- k query on the ingested cameras and obtain the response within milliseconds. A preliminary video introduction of DoveDB is available at https://www.youtube.com/watch?v=N139dEyvAJk

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DoveDB:一个声明性和低延迟的视频数据库

关于管理大型摄像机视频数据的可用性和效率，我们演示了DoveDB，一个声明性和低延迟的视频数据库。我们设计了一种更全面的视频查询语言，称为VMQL，以改进以前的类sql语言的表达能力，这些语言增加了面向模型的管理和部署功能。我们还提出了一种轻量级的摄取方案来提取所有运动对象的轨迹，并建立语义索引以促进高效的查询处理。对于用户交互，我们构建了一个模拟环境，在道路网络中部署了120个摄像头，并演示了三个有趣的场景。使用VMQL，用户可以1)使用类似sql的语句训练可视化模型，并将其同时部署在数十台目标相机上进行在线推理;2)向目标摄像机提交多目标跟踪(MOT)请求，存储接收结果并建立语义索引;3)对摄取的相机发出聚合或top- k查询，并在毫秒内获得响应。DoveDB的初步视频介绍可以在https://www.youtube.com/watch?v=N139dEyvAJk上获得

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来源期刊

Proceedings of the Vldb Endowment Computer Science-General Computer Science

CiteScore

7.70

自引率

0.00%

发文量

期刊介绍： The Proceedings of the VLDB (PVLDB) welcomes original research papers on a broad range of research topics related to all aspects of data management, where systems issues play a significant role, such as data management system technology and information management infrastructures, including their very large scale of experimentation, novel architectures, and demanding applications as well as their underpinning theory. The scope of a submission for PVLDB is also described by the subject areas given below. Moreover, the scope of PVLDB is restricted to scientific areas that are covered by the combined expertise on the submission’s topic of the journal’s editorial board. Finally, the submission’s contributions should build on work already published in data management outlets, e.g., PVLDB, VLDBJ, ACM SIGMOD, IEEE ICDE, EDBT, ACM TODS, IEEE TKDE, and go beyond a syntactic citation.