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

我们生活中的相机数量和相机系统的规模不断增加，因为相机系统的技术进步和价格下降创造了新的机会和应用。除了个人用途外，摄像机还广泛用于军事、公共和商业应用，用于监视和统计数据收集。据估计，美国有3000万个监控摄像头，每周拍摄40亿小时的镜头。除了传统的监控用途，谷歌眼镜等项目正在推动具有本地处理和通信功能的微型和低成本摄像头的发展。对于未来的摄像机系统，组件之间的本地智能和自主协作将提供解决更复杂任务的能力，这需要统一的视角来同时解决硬件/软件协同设计、实时操作、高精度以及运行时的自协调和自适应等挑战。本项目为设计提供了一种整体和新颖的方法，一组协作嵌入式智能摄像头的部署和自我协调，目标是以最高的精度和最小的延迟监控大面积区域。一个目标是为嵌入式智能相机设计合成方法和计算基础设施，允许硬件重构和硬件和软件之间的系统任务交换。另一个目标是开发自我配置的方法，以自主适应系统的行为和优化处理运行时的环境变化，包括节点failure.This研究预计能够开发新的实时，全自动，协作和高精度的摄像机系统，通过提供一个系统的方法来设计和部署这样的系统，并在实验室和校园规模测试新的方法。潜在的应用包括智能监控系统、基于多摄像头的驾驶辅助系统、养老院辅助、基于3D重建的生产线质量控制和远程手术。该项目还将研究与两个机构的本科生和研究生课程相结合，并通过阿肯色州工程职业意识计划，阿肯色州路易斯斯托克斯少数民族参与联盟和乔治华盛顿卡弗项目以及锡拉丘兹大学的WiSE计划，为增加代表性不足的群体的参与做出贡献。 来自代表性不足的群体的学生将被招募并参与协作多摄像机网络的设计，实施和部署。

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.