III: Small: Distributed Semantic Information Processing Applied to Camera Sensor Networks

在许多应用中，传感器网络可用于监测大的地理区域。这通常会产生大量的数据，这些数据需要进行关联、汇总和分类，以便对所监测的现象进行语义上有意义的描述。该项目的长期指导愿景是一个分布式网络，可以在很长一段时间内以可扩展的方式自主执行此分析。作为一个具体的应用，本研究的重点是智能相机网络的节点是静态的或机器人代理的一部分。计划中的工作将使系统更有效，更准确，更有弹性。开发的算法将得到广泛的应用，包括安全（连续真实的时间检测可疑人员）和物联网。作为更广泛影响的一部分，该项目将制作教育材料，向K12观众解释该项目的科学成果。这项研究将引入一个新的框架，用于在智能摄像机网络中一致，高效和可靠地提取语义信息。该项目将围绕三个支柱：（i）解决一致的多相机特征匹配问题：发现在一个节点上看到的特征是否被其他相机节点看到，如果是，使用分散式散列的变体找到最一致的对应关系;（ii）自主找到新兴模式（例如，来自不同个体的图像）通过特征聚类，使用用于计算k-中心点的分布式算法;（iii）采用新的分布式决策树推理范例，该范例允许网络在整个网络中传播计算的同时对世界（例如，个体的身份）做出一致的决策。该项目网站将提供对工作成果的访问，包括论文、可下载代码和教育材料。

In many applications, sensor networks can be used to monitor large geographical regions. This typically produces large quantities of data that need to be associated, summarized and classified in order to arrive to a semantically meaningful descriptions of the phenomena being monitored. The long-term guiding vision of this project is a distributed network that can perform this analysis autonomously, over long periods of times, and in a scalable way. As a concrete application, this research focuses on smart camera networks with nodes that are either static or part of robotic agents. The planned work will result in systems that are more efficient, accurate, and resilient. The algorithms developed will find wide applications, including in security (continuously detecting suspicious individuals in real time) and the Internet of Things. As part of the broader impacts, the project will produce educational material to explain the scientific results of the project to a K12 audience.This research will introduce a novel framework for consistently, efficiently and reliably extracting semantic information across a network of smart cameras. The project will revolve around three pillars: (i) solving the consistent multi-camera feature matching problem: find if a feature seen at a node has been seen by other camera nodes, and if yes, find the most consistent correspondences using a variation of decentralized hashing; (ii) autonomously finding emerging patterns (e.g., images from different individuals) through feature clustering, using a distributed algorithm for computing k-medoids; (iii) employing a new distributed decision tree inference paradigm that allows the network to take consistent decisions about the world (for example, the identity of an individual) while spreading computations across the network. The project website will provide access to the results of the work, including papers, downloadable code, and educational material.