{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T05:21:26Z","timestamp":1672291286200},"reference-count":8,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>We present VISE, or Vehicle Image Search Engine, to support the fast search of similar vehicles from low-resolution traffic camera images. VISE can be used to trace and locate vehicles for applications such as police investigations when high-resolution footage is not available. Our system consists of three components: an interactive user-interface for querying and browsing identified vehicles; a scalable search engine for fast similarity search on millions of visual objects; and an image processing pipeline that extracts feature vectors of objects from video frames. We use transfer learning technique to integrate state-of-the-art Convolutional Neural Networks with two different refinement methods to achieve high retrieval accuracy. We also use an efficient high-dimensional nearest neighbor search index to enable fast retrieval speed. In the demo, our system will offer users an interactive experience exploring a large database of traffic camera images that is growing in real time at 200K frames per day.<\/jats:p>","DOI":"10.14778\/3352063.3352080","type":"journal-article","created":{"date-parts":[[2019,9,18]],"date-time":"2019-09-18T18:36:11Z","timestamp":1568831771000},"page":"1842-1845","source":"Crossref","is-referenced-by-count":1,"title":["VISE"],"prefix":"10.14778","volume":"12","author":[{"given":"Hyewon","family":"Choi","sequence":"first","affiliation":[{"name":"University of Toronto"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erkang","family":"Zhu","sequence":"additional","affiliation":[{"name":"University of Toronto"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arsala","family":"Bangash","sequence":"additional","affiliation":[{"name":"University of Toronto"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ren\u00e9e J.","family":"Miller","sequence":"additional","affiliation":[{"name":"Northeastern University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Database Speed Comparison. https:\/\/www.sqlite.org\/speed.html. Accessed: 2019-03-14.  Database Speed Comparison. https:\/\/www.sqlite.org\/speed.html. Accessed: 2019-03-14."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_1_3_1","first-page":"379","volume-title":"NIPS","author":"Dai J.","year":"2016","unstructured":"J. Dai , Y. Li , K. He , and J. Sun . R-FCN: object detection via region-based fully convolutional networks . In NIPS , pages 379 -- 387 , 2016 . J. Dai, Y. Li, K. He, and J. Sun. R-FCN: object detection via region-based fully convolutional networks. In NIPS, pages 379--387, 2016."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788621"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939728"},{"key":"e_1_2_1_7_1","volume-title":"Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. CoRR, abs\/1603.09320","author":"Malkov Y. A.","year":"2016","unstructured":"Y. A. Malkov and D. A. Yashunin . Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. CoRR, abs\/1603.09320 , 2016 . Y. A. Malkov and D. A. Yashunin. Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. CoRR, abs\/1603.09320, 2016."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3352063.3352080","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:40:35Z","timestamp":1672224035000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3352063.3352080"}},"subtitle":["vehicle image search engine with traffic camera"],"short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":8,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2019,8]]}},"alternative-id":["10.14778\/3352063.3352080"],"URL":"https:\/\/doi.org\/10.14778\/3352063.3352080","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2019,8]]}}}