SST: Minimally-Attended Integrated Visual Surveillance Network

SST：少有人值守的集成视觉监控网络

• 批准号: 0428042
• 负责人: Ralph Etienne-Cummings
• 金额: --
• 依托单位: Johns Hopkins University
• 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=0428042&HistoricalAwards=false
• 关键词: SST; Minimally; Attended; Integrated; Visual

This Sensor proposal focuses on the development of sensory information processing front-ends for a minimally-attended visual surveillance network. We intend to use mixed-signal hardware computation methods in a system-on-a-chip architecture. Image processing algorithms are implemented at the focal-plane, using compact circuits that operate at microwatt power levels to minimize battery weight and form factor. Furthermore, the sensory front-end must demonstrate operational autonomy (i.e. in decision making) and robustness (i.e. to changes in environmental conditions).Intellectual Merit: The proposed system will replace traditional computer vision systems (cameras, digitizers and processors) with a computational sensor. Using wide-dynamic range imaging, with local gain control, circumvents the limitations of standard cameras. Spatiotemporal feature extraction is used to highlight moving targets. The shape formed by the features is identified using kernel learning in silicon; alarms are generated based on the similarity of the targets to preprogrammed shapes. Without these types of smart, ultra-low power, compact imaging and computational microsystems, practical sensor networks for visual surveillance may not be realizable.Broader Impact: The sensors are primarily intended for surveillance of large, remotely located areas, where limited manpower is available, e.g. border patrolling. Their low power and small size obviates a variety of mobile applications.The multi-disciplinary nature of this work will result in the development a pipeline of students, educators and researchers with the broad skills required to succeed in modern high technology industry and academics. Their education will be rounded with exposure to issues in homeland security, privacy rights and international law.

该传感器提案的重点是开发用于最少参与的视觉监控网络的传感信息处理前端。我们打算在片上系统架构中使用混合信号硬件计算方法。图像处理算法在焦平面上实现，使用在微瓦功率水平下工作的紧凑电路，以最大限度地减少电池重量和形状因子。此外，传感器前端必须表现出操作自主性（即在决策）和鲁棒性（即在环境条件的变化）。智力优点：拟议的系统将取代传统的计算机视觉系统（相机，数字化仪和处理器）与计算传感器。使用具有局部增益控制的宽动态范围成像，规避了标准相机的限制。采用时空特征提取方法突出运动目标。特征形成的形状是使用硅中的内核学习来识别的;警报是基于目标与预编程形状的相似性生成的。如果没有这些类型的智能，超低功耗，紧凑的成像和计算微系统，实用的传感器网络的视觉surveillance.Broader影响：传感器主要用于大型，偏远地区的监视，其中有限的人力资源，例如边境巡逻。它们的低功耗和小尺寸排除了各种移动的应用。这项工作的多学科性质将导致学生、教育工作者和研究人员的发展，他们具有在现代高科技产业和学术界取得成功所需的广泛技能。他们的教育将通过接触国土安全，隐私权和国际法问题来实现。