CSR: Medium: Collaborative Research: Self-Coordination in Cooperative Smart Camera Networks Incorporating System-On-Chip Reconfiguration

CSR：媒介：协作研究：结合片上系统重新配置的协作智能相机网络中的自协调

• 批准号: 1302596
• 负责人: Christophe Bobda
• 金额: $ 35.78万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1302596&HistoricalAwards=false
• 关键词: CSR; Medium; Collaborative; Research; Self

The number of cameras in our lives and the scale of camera systems are continuously increasing as technological advances and falling prices in camera systems create new opportunities and applications. In addition to personal uses, cameras are widely employed in military, public and commercial applications for surveillance and statistics gathering. There are an estimated 30 million surveillance cameras in the U.S. capturing 4 billion hours of footage a week. Besides the traditional use of cameras for surveillance purposes, projects such as Google Glass are driving the development of miniature and low-cost cameras with local processing and communication capabilities. For future camera systems, local intelligence and autonomous collaboration among components will provide the capability to solve more complex tasks, which requires a unifying perspective to simultaneously address the challenges of hardware/software co-design, real-time operation, high accuracy and self-coordination and self-adaptation in run-time.This project provides a holistic and novel approach for the design, deployment and self-coordination of a set of collaborative embedded smart cameras, with the goal of monitoring large areas with the highest accuracy and smallest latency. One objective is designing synthesis approaches and computing infrastructure for the embedded smart cameras that allow hardware restructuring and systematic swapping of tasks between hardware and software on-the-fly. Another objective is to develop self-configuration approaches to autonomously adapt system behavior and optimally deal with run-time environmental changes, including node failures.This research is expected to enable development of new real-time, fully automated, collaborative and highly accurate camera systems by providing a systematic approach for the design and deployment of such systems, and testing new methods at laboratory and campus scales. Potential applications include smart surveillance systems, multi-camera-based driver assistance systems, assistance in nursing homes, quality control on production lines based on 3D reconstruction, and remote surgery. The project also integrates research with the undergraduate and graduate programs of two institutions and contributes towards increasing the involvement of under-represented groups through the University of Arkansas Engineering Career Awareness Program, Arkansas Louis Stokes Alliance for Minority Participation and George Washington Carver Project, and the WiSE program at Syracuse University. Students from under-represented groups are to be recruited and involved in the design, implementation, and deployment of collaborative multi-camera networks.

随着技术进步和相机系统价格下跌，我们生活中的相机数量和相机系统的规模不断增加，创造了新的机会和应用。除个人用途外，相机还广泛用于军事，公共和商业应用中，用于监视和统计收集。美国估计有3000亿次监视摄像机每周捕获40亿小时的录像。除了传统使用摄像机来监视目的外，诸如Google Glass之类的项目还可以推动具有本地处理和通信功能的微型和低成本相机的开发。对于将来的摄像头系统，组件之间的本地智力和自主协作将提供解决更复杂的任务的能力，这需要统一的观点同时解决硬件/软件共同设计，实时操作，高准确性和自我协调和自适应的挑战，并在运行中进行自我适应性，以实现整体和新颖的方法。相机，目的是监视具有最高准确性和最小延迟的大面积。一个目的是为嵌入式智能摄像机设计综合方法和计算基础架构，该摄像头允许硬件重组和系统地交换硬件和软件之间的任务。另一个目的是开发自主适应系统行为的自我配置方法，并最佳地处理运行时的环境变化，包括节点故障。这项研究有望通过为在实验室和露营阶段的新方法和测试新方法提供系统的系统来开发新的实时，完全自动化，协作和高度准确的相机系统。潜在的应用包括智能监视系统，基于多相机的驾驶员援助系统，疗养院的帮助，基于3D重建的生产线的质量控制以及远程手术。该项目还将研究与两家机构的本科和研究生计划融为一体，并通过阿肯色大学的工程职业意识计划，阿肯色州路易斯·斯托克斯联盟（Arkansas Louis Stokes Alliance）参加少数群体参与和乔治·华盛顿·卡佛（George Washington Carver Project），以及锡拉丘斯大学（Syracuse University of Syracuse University of Syracuse University of Syracuse University of Syracuse University），有助于增加代表性不足的团体的参与。 来自代表性不足的小组的学生应招募并参与协作多摄像机网络的设计，实施和部署。