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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.<\/jats:p>","DOI":"10.14778\/3611540.3611583","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:32:37Z","timestamp":1694777557000},"page":"3910-3913","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["DeepVQL: Deep Video Queries on PostgreSQL"],"prefix":"10.14778","volume":"16","author":[{"given":"Dong June","family":"Lew","sequence":"first","affiliation":[{"name":"Kunsan National University"}]},{"given":"Kihyun","family":"Yoo","sequence":"additional","affiliation":[{"name":"Kunsan National University"}]},{"given":"Kwang Woo","family":"Nam","sequence":"additional","affiliation":[{"name":"Kunsan National University"}]}],"member":"320","published-online":{"date-parts":[[2023,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389692"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00934"},{"key":"e_1_2_1_3_1","unstructured":"Kai Chen Jiaqi Wang Jiangmiao Pang Yuhang Cao Yu Xiong Xiaoxiao Li Shuyang Sun Wansen Feng Ziwei Liu Jiarui Xu et al. 2019. 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