ITR - (NHS) - (int) Multi-level, Active Attention Surveillance

ITR - (NHS) - (int) 多层次主动注意力监控

• 批准号: 0428249
• 负责人: Richard Parent
• 金额: $ 130万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-10-01 至 2008-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0428249&HistoricalAwards=false
• 关键词: ITR; NHS; int; Multi; level

ABSTRACTThis proposal seeks to advance security surveillance monitoring by introducing event-based reasoning. The team will use a formal event-discovery protocol to uncover event categories and the temporal structure of events. This results in an event template hierarchy. The event template hierarchy is supported by the enabling technologies of smart sensors, a reconfigurable network, and the use of persistent models for tracking. The result is an autonomous sensor network that can be effectively coupled to human operators in order to allow top-down control of the resources as well as the ability to modify the models for event and background activities. While the methodology is suitable for a wide variety of application domains, the work is grounded in a campus security and surveillance paradigm.By integrating research from Cognitive Science, Geography, and Computer Science (Graphics, and Vision), the team can create a paradigmatic shift in the way that surveillance systems are viewed and developed. The data stream is no longer composed merely of video and perhaps some low-level alarms; the focus is now extended to include events. Data and information no longer move toward a usersitting in front of a wall of monitors. Event contexts, set by higher-level events as well as by operators-in-the-loop, direct and focus attention in order to detect differences from a dynamic model of background activity. The result is that the information is more meaningful, the surveillance systems more focused, and the cognitive skills of the operators more efficiently utilized. A prototype system will be made available for pertinent security personnel to train and test. The work will contribute to training methodologies of security personnel. Under the purview of broader impact, the proposed work strives to include under-represented and minority student groups through targeted training in the use of video technology. Finally if successful, event-based strategic surveillance networks can provide alternatives to racial profiling. The individuals are judged only by their actions as encoded in the event models.

摘要本提案旨在通过引入基于事件的推理来推进安全监视监控。该团队将使用正式的事件发现协议来揭示事件类别和事件的时间结构。这将生成事件模板层次结构。事件模板层次结构由智能传感器、可重构网络和使用持久模型进行跟踪的使能技术支持。其结果是一个自主的传感器网络，可以有效地耦合到人类操作员，以允许自上而下的资源控制以及修改事件和背景活动的模型的能力。虽然该方法适用于各种应用领域，但该工作以校园安全和监控范式为基础，通过整合认知科学，地理学和计算机科学（图形和视觉）的研究，该团队可以在监控系统的查看和开发方式中创造范式转变。数据流不再仅仅由视频和一些低级别警报组成;现在重点扩展到包括事件。数据和信息不再流向坐在显示器前的用户。事件背景，由更高级别的事件以及操作员设置在循环中，直接和集中注意力，以检测背景活动的动态模型的差异。其结果是，信息更有意义，监视系统更集中，操作员的认知技能得到更有效的利用。将提供一个原型系统，供有关安保人员培训和测试。这项工作将有助于改进安保人员的培训方法。在更广泛的影响范围内，拟议的工作力求通过有针对性的视频技术使用培训，将代表性不足的学生群体和少数民族学生群体包括在内。最后，如果成功的话，基于事件的战略监测网络可以提供种族貌相的替代办法。个体仅通过事件模型中编码的行为来判断。