{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T00:13:31Z","timestamp":1723076011657},"reference-count":57,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"name":"National Key Research and Development Project of China","award":["2022YFF0902000"]},{"name":"Key Research Program of Zhejiang Province","award":["2023C01037"]},{"name":"CCF-Huawei Populus Grove Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2024,3,12]]},"abstract":"Scalable video query optimization has re-emerged as an attractive research topic in recent years. The OTIF system, a video database with cutting-edge efficiency, has introduced a new paradigm of utilizing view materialization to facilitate online query processing. Specifically, it stores the results of multi-object tracking queries to answer common video queries with sub-second latency. However, the cost associated with view materialization in OTIF is prohibitively high for supporting large-scale video streams.<\/jats:p>\n In this paper, we study efficient MOT-based view materialization in video databases. We first conduct a theoretical analysis and establish two types of optimality measures that serve as lower bounds for video frame sampling. In order to minimize the number of processed video frames, we propose a novel predictive sampling framework, namely LEAP, exhibits near-optimal sampling performance. Its efficacy relies on a data-driven motion manager that enables accurate trajectory prediction, a compact object detection model via knowledge distillation, and a robust cross-frame associator to connect moving objects in two frames with a large time gap.<\/jats:p>\n Extensive experiments are conducted in 7 real datasets, with 7 baselines and a comprehensive query set, including selection, aggregation and top-k queries. The results show that with comparable query accuracy to OTIF, our LEAP can reduce the number of processed video frames by up to 9\u00d7 and achieve 5\u00d7 speedup in query processing time. Moreover, LEAP demonstrates impressive throughput when handling large-scale video streams, as it leverages a single NVIDIA RTX 3090ti GPU to support real-time MOT-based view materialization from 160 video streams simultaneously.<\/jats:p>","DOI":"10.1145\/3639274","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T22:51:32Z","timestamp":1711493492000},"page":"1-27","source":"Crossref","is-referenced-by-count":0,"title":["Predictive and Near-Optimal Sampling for View Materialization in Video Databases"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"http:\/\/orcid.org\/0009-0002-3117-1243","authenticated-orcid":false,"given":"Yanchao","family":"Xu","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9964-2470","authenticated-orcid":false,"given":"Dongxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9927-6925","authenticated-orcid":false,"given":"Shuhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1866-9197","authenticated-orcid":false,"given":"Sai","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"http:\/\/orcid.org\/0009-0002-9855-3211","authenticated-orcid":false,"given":"Zexu","family":"Feng","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7483-0045","authenticated-orcid":false,"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2024,3,26]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00132"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-018--9826--2"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389692"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3517835"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3517857"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3526181"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1979.4766909"},{"key":"e_1_2_2_8_1","unstructured":"Jeff Erickson. 2019. 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