NeTS-NoSS: Sensing in Three Dimensions with Smart Cameras

该项目正在构建协作的摄像机网络，可用于重建三维（3D）特征，从新的视角生成图像，将轨迹或物体与已知模式进行匹配，或将这些任务联合收割机结合起来，以提供一个功能强大、灵活的监控系统。高数据速率和精确校准要求提出了早期，更简单的传感器网络。将视频数据从数百或数千台摄像机传输到中央位置进行处理所需的带宽将是巨大的。相反，该项目正在构建低功耗智能摄像机，这些摄像机可以真实的处理视频数据，从协作相机的原始图像中提取特征和3D几何形状。 这些压缩的结果，仍然有点带宽密集，存储在网络中，直到用户需要。 基于内容的路由技术允许对数据的时空表示进行查询。 查询处理发生在网络中，大大降低了带宽需求。摄像机网络必须精确校准，发现和跟踪对象，将视图请求路由到可行的摄像机，并避免不必要的传输。 内容路由技术，将允许相机找到共同的特点-关键的校准，搜索和跟踪。这些技术允许功能存储和处理附近的采集点，避免浪费通信。 集成的、特定于应用的压缩技术进一步降低了开销。该项目还旨在简化构建3D传感器网络应用的工程工作。一个时空“立方体”的抽象被用来表示整个传感器网络的数据，在整个时间。 声明性语言用于指定搜索和跟踪的特征模式。 高级特征可以被描述为较简单特征在时空中的组合。 这些描述被编译为使用以数据为中心的协议来实现数据选择、搜索或跟踪，而无需数据集中。

This project is building cooperating networks of cameras that can be used to reconstruct three-dimensional (3D) features, produce images from novel viewpoints, match trajectories or objects against known patterns, or combine these tasks to provide a powerful, flexible monitoring system.High data rates and precise calibration requirements present challenges that are not faced by earlier, simpler sensor networks.The bandwidth required to transmit video data from hundreds or thousands of cameras to a central location for processing would be enormous.Instead, this project is building low-power smart cameras that process video data in real time, extracting features and 3D geometry from the raw images of cooperating cameras. These compressed results, still somewhat bandwidth intensive, are stored in the network until required by users. Content-based routing techniques enable queries against a space-time representation of the data. Query processing occurs in-network, greatly reducing bandwidth requirements.Camera networks must calibrate precisely, discover and track objects, route view requests to viable cameras, and avoid unnecessary transmissions. Content-routing techniques that will allow cameras to find common features---critical for calibration, search, and tracking.These techniques allow features to be stored and processed near their acquisition point, avoiding wasted communication. Integrated, application-specific compression techniques further reduce overhead.This project also aims to simplify the engineering effort in building 3D sensornet applications. A space-time ``cube'' abstraction is used to represent the data of the entire sensornet, throughout time. A declarative language is used to specify feature patterns for search and tracking. High-level features can be described as compositions in space-time of simpler features. These descriptions are compiled to use the data-centric protocols to implement data selection, search, or tracking without data centralization.