A Lightweight Event-Driven Network of Biomimetic Image Sensors

轻量级事件驱动仿生图像传感器网络

• 批准号: 0622133
• 负责人: Eugenio Culurciello
• 金额: --
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0622133&HistoricalAwards=false
• 关键词: Lightweight; Event; Driven; Network; Biomimetic

This proposal focuses on the use of biomimetic image sensors for an image sensor network. Custom image sensors will provide a reduced visual representation and enable the sensor network to interpret behavior and activity in an indoor setting. The research emphasis is to 1) parse out sensor observations into a set of distinguishable actions that can be understood by machines, 2) preserve privacy by extracting data from a scene without acquiring images and by making image reconstruction difficult for perpetrators, 3) provide ultra-low power components and a lightweight sensor network of images that operates using symbolic information over low bandwidth wireless links.Intellectual Merit: The research enables image parsing and interpretation on sensor networks. A new generation of probabilistic detection algorithms that operate on prioritized data to produce fast detections with limited information will be developed. The algorithms will be able to execute on the low-end microcontrollers used on sensor nodes. Using modular and biomimetic approaches, the PIs will design low-power image sensors that will perform efficient data extraction at the sensor level. Their design approach will reduce the computation and communications required by image sensors and provide an orthogonal methodology to conventional machine vision. The proposed research is required to advance the technology of mobile cameras for monitoring indoor assisted-living environments.Broader Impacts: The image sensor network is the building block of building security, wide-area security, national security, monitoring health of civil structures as bridges and highways, observing natural environments and habitats, detecting severe natural events. The proposed image sensor network will be used in an assisted-living environment to monitor aging patients during everyday activity. The applied nature of the project is an attractive way to train students, particularly women and under-represented groups, and to motivate them to pursue careers in research and academia.

本提案的重点是在图像传感器网络中使用仿生图像传感器。自定义图像传感器将提供简化的视觉表示，并使传感器网络能够解释室内环境中的行为和活动。研究重点是1)将传感器观测解析为一组可被机器理解的可区分动作，2)通过从场景中提取数据而不获取图像并使肇事者难以重建图像来保护隐私，3)提供超低功耗组件和轻量级图像传感器网络，该网络使用低带宽无线链路上的符号信息运行。智力优势：该研究使传感器网络上的图像解析和解释成为可能。将开发新一代的概率检测算法，该算法对优先数据进行操作，从而在有限的信息下产生快速检测。该算法将能够在传感器节点上使用的低端微控制器上执行。使用模块化和仿生方法，pi将设计低功耗图像传感器，在传感器级执行有效的数据提取。他们的设计方法将减少图像传感器所需的计算和通信，并为传统的机器视觉提供正交方法。建议的研究需要推进移动相机技术，以监测室内辅助生活环境。更广泛的影响：图像传感器网络是建筑安全、广域安全、国家安全、监测桥梁和公路等民用结构的健康状况、观察自然环境和栖息地、检测严重自然事件的基石。所提出的图像传感器网络将用于辅助生活环境中监测老年患者的日常活动。该项目的应用性质是培养学生，特别是妇女和代表性不足的群体，并激励他们从事研究和学术事业的一种有吸引力的方式。