{"title":"DeepVQL: Deep Video Queries on PostgreSQL","authors":"Dong June Lew, Kihyun Yoo, Kwang Woo Nam","doi":"10.14778/3611540.3611583","DOIUrl":null,"url":null,"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.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Vldb Endowment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14778/3611540.3611583","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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.
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.
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