EAGER: Video Analytics in Large Heterogeneous Repositories

EAGER：大型异构存储库中的视频分析

• 批准号: 1262121
• 负责人: David Doermann
• 金额: $ 21.42万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1262121&HistoricalAwards=false
• 关键词: EAGER; Video; Analytics; Large; Heterogeneous

The planned research will take video analysis indexing and retrieval in a new and promising direction. The research is driven by the need for intelligence analysts to be able to express video queries more efficiently than traditional relevance feedback and to be able to "provide more expressive queries that include "nouns" and "verbs" as they would with human language. While still constrained, the approach goes a long way toward bridging the gap between traditional relevance feedback based only on assumed relationships in the image, and full human language queries. The graduate students involved in the project will be required to publish in international conferences and journals and will likely use this research as a basis for their dissertations. Other impacts of this work include the mentoring of graduate students and the inclusion of junior personnel in the management of the project. Research will be disseminated through local, national, and international meetings and journals. The team will also install a server and public interface for demonstration on existing datasets. The system will be accessible through the web on limited datasets and on the full dataset by request.

计划的研究将把视频分析、索引和检索带向一个新的、有前途的方向。这项研究是由情报分析师的需求推动的，他们希望能够比传统的相关反馈更有效地表达视频查询，并能够“像使用人类语言一样提供更有表现力的查询，包括“名词”和“动词”。虽然仍然受到限制，但该方法在弥合仅基于图像中假设关系的传统相关性反馈与完整的人类语言查询之间的差距方面走了很长的路。参与该项目的研究生将被要求在国际会议和期刊上发表论文，并可能将此研究作为其学位论文的基础。这项工作的其他影响包括对研究生的指导和在项目管理中纳入初级人员。研究将通过地方、国家和国际会议和期刊传播。该团队还将安装一个服务器和公共接口，用于在现有数据集上进行演示。该系统将通过网络访问有限的数据集，并根据请求访问完整的数据集。