DeepVQL: Deep Video Queries on PostgreSQL

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:10.14778/3611540.3611583

Dong June Lew, Kihyun Yoo, Kwang Woo Nam

引用次数: 0

Abstract

The recent development of mobile and camera devices has led to the generation, sharing, and usage of massive amounts of video data. As a result, deep learning technology has gained attention as an alternative for video recognition and situation judgment. Recently, new systems supporting SQL-like declarative query languages have emerged, focusing on developing their own systems to support new queries combined with deep learning that are not supported by existing systems. The proposed DeepVQL system in this paper is implemented by expanding the PostgreSQL system. DeepVQL supports video database functions and provides various user-defined functions for object detection, object tracking, and video analytics queries. The advantage of this system is its ability to utilize queries with specific spatial regions or temporal durations as conditions for analyzing moving objects in traffic videos.

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DeepVQL:基于PostgreSQL的深度视频查询

最近移动和摄像设备的发展导致了大量视频数据的产生、共享和使用。因此，深度学习技术作为视频识别和态势判断的替代方案受到了关注。最近，支持类似sql的声明式查询语言的新系统出现了，它们专注于开发自己的系统，以支持现有系统不支持的结合深度学习的新查询。本文提出的DeepVQL系统是通过扩展PostgreSQL系统来实现的。DeepVQL支持视频数据库功能，并提供各种自定义函数，用于对象检测、对象跟踪和视频分析查询。该系统的优点是能够利用具有特定空间区域或时间持续时间的查询作为分析交通视频中移动物体的条件。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.