基于云活动的智能(VOCABI)视频观测

Erik Blasch, Phil DiBona, M. Czajkowski, Kevin Barry, R. Rimey, J. Freeman, Kevin Newman, Alexander J. Aved, M. Hinman
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引用次数: 4

摘要

通过减小传感器尺寸、成本和功率,视频图像的可用性使收集机会激增。随着图像量的增加,有必要了解视频对诸如基于活动的智能(Activity-Based Intelligence, ABI)、情况理解和基于事件的处理等应用程序的有用性。在本文中,我们以云技术为重点,探讨了视频观测的一些新兴发展。云技术支持多种算法的集成、大型数据集的存储、多媒体索引以及人与机器之间的工作流机会。我们重点介绍了多种工具,如GeoFlix™、应用知识接口到算法(AKITA™)和使用臭氧小部件框架(OWF)进行许可监视、数据到决策和信息融合的操作环境的智能准备(IPOE)。这些工具支持数据分析、算法比较和用户定义的可视化。给出了一个通过有计划的视频观测实现目标定位和跟踪的实例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Video observations for cloud activity-based intelligence (VOCABI)
The availability of video imagery through reduction in sensor size, cost, and power has enabled an explosion of collection opportunities. With the increased amount of imagery there is a need to understand the usefulness of video for applications such as Activity-Based Intelligence (ABI), situation understanding, and event-based processing. In this paper, we explore some of the emerging developments in video observations with a focus on cloud technology. Cloud technology supports integration of multiple algorithms, storage of large data sets, indexing over multimedia, and workflow opportunities between humans and machines. We highlight multiple tools such as GeoFlix™, Application Knowledge Interface To Algorithms (AKITA™), and Intelligence Preparation of the Operational Environment (IPOE) using the Ozone Widget Framework (OWF) for permissive surveillance, data to decisions, and information fusion. These tools enable data analytics, algorithm comparison, and user-defined visualizations. An example is presented for target localization and tracking through planned video observations.
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