Hydra: A Robust and Self Managing Video Sensing System for Retrospective Surveillance (UND_FY05_055)

Hydra：用于回顾性监控的稳健且自我管理的视频传感系统 (UND_FY05_055)

• 批准号: 0515674
• 负责人: Surendar Chandra
• 金额: $ 22.3万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-03-15 至 2007-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0515674&HistoricalAwards=false
• 关键词: Hydra; Robust; Self; Managing; Video

AbstractNSF-0515674Chandra, SurendarThe effort focuses on developing recognition algorithms that use a large number of multimedia sensors that are deployed as needed in an ad hoc fashion. The goal is to design algorithms that connect the various uncoordinated views and deduce the global state. Rather than requiring real time scene analysis, the proposed system stores the scenes in high fidelity for specific durations in in-situ storage bricks. Retrospective algorithms search and leverage the various views in order to detect complex states that are missed by analyzing a single stream. Storing the stream also allows the system to analyze complex behaviors that require behavior analysis over extended periods of time. A self managing infrastructure for profuse media capture and storage will be developed including efficient mechanisms for the sensor components to search and locate other components using an unstructured peer-to-peer overlay. Operating systems mechanisms that allow these sensor components to balance their local resource consumption with global end-to-end requirements will be designed. Pattern recognition tasks such as semantic tagging of actor trajectories, multi-camera moving object tracking as well as mechanisms for integrate multimedia data sets to extract patterns of interest are also planned. The proposed research will enable recognition tasks that are not possible in traditionalsystem that require real time processing. The focus on sensors built with commodity components offer the potential for immediate impact to the user community. These systems promise to have significant economic, environmental, and societal impacts. The research prototypes developed will be disseminated freely. The educational aspect of this proposal will focus on integration of the research results into an experimental systems curriculum with graduate training and undergraduate summer research mentorships, especially for women and underrepresented students.

这一努力的重点是开发使用大量多媒体传感器的识别算法，这些传感器按需要以特别的方式部署。我们的目标是设计算法，连接各种不协调的意见，并推导出全球的状态。而不是要求真实的时间场景分析，所提出的系统存储在现场存储砖在特定的持续时间的场景在高保真度。回溯算法搜索并利用各种视图，以检测分析单个流时遗漏的复杂状态。存储流还允许系统分析需要在延长的时间段内进行行为分析的复杂行为。将开发用于丰富媒体捕获和存储的自我管理基础设施，包括传感器组件使用非结构化对等覆盖搜索和定位其他组件的有效机制。操作系统的机制，使这些传感器组件，以平衡其本地资源消耗与全球端到端的要求将被设计。模式识别任务，如语义标记的演员轨迹，多摄像机移动对象跟踪以及机制，集成多媒体数据集，以提取感兴趣的模式也计划。这项研究将使识别任务，是不可能在传统的系统，需要真实的时间处理。对使用商品组件构建的传感器的关注为用户社区提供了直接影响的潜力。这些系统有望产生重大的经济、环境和社会影响。开发的研究原型将免费传播。这项建议的教育方面将侧重于将研究成果纳入实验系统课程，并提供研究生培训和本科生暑期研究指导，特别是针对妇女和代表性不足的学生。