VOCAL: Video Organization and Interactive Compositional AnaLytics
VOCAL: Video Organization and Interactive Compositional AnaLytics
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发表时间：
2022
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通讯作者：
Maureen Daum;Enhao Zhang;Dong He;M. Balazinska;Brandon Haynes;Ranjay Krishna;Apryle Craig;Aaron J. Wirsing
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作者：
Maureen Daum;Enhao Zhang;Dong He;M. Balazinska;Brandon Haynes;Ranjay Krishna;Apryle Craig;Aaron J. Wirsing
当前的视频数据库管理系统（VDBMS）无法支持不同领域中越来越多的视频数据集，因为这些系统假设干净的数据并依赖于预训练的模型来检测已知的对象或动作。现有的系统也缺乏良好的组合查询，寻求事件组成的多个对象具有复杂的空间和时间关系的支持。在本文中，我们提出了VOCAL，一个虚拟数据库管理系统的愿景，支持高效的数据清理，探索和组织，以及组合查询，即使没有预先训练的模型存在提取语义内容。这些技术利用优化来最小化用户所需的手动工作。
Current video database management systems (VDBMSs) fail to support the growing number of video datasets in diverse domains because these systems assume clean data and rely on pretrained models to detect known objects or actions. Existing systems also lack good support for compositional queries that seek events consisting of multiple objects with complex spatial and temporal relationships. In this paper, we propose VOCAL, a vision of a VDBMS that supports efficient data cleaning, exploration and organization, and compositional queries, even when no pretrained model exists to extract semantic content. These techniques utilize optimizations to minimize the manual effort required of users.