NeTS: Small: Distributed Solutions to Smart Camera Networks

智能摄像头网络（scn）融合了计算机视觉、分布式处理和传感器网络学科，通过分布式感知和协同网络处理提供有价值的信息，解决多摄像头应用中的问题。传感器网络中的协作不仅需要补偿每个传感器节点的处理，传感，能量和带宽限制，而且还需要提高网络的准确性和鲁棒性。scn中的协同处理比非常规的标量传感器网络（ssn）更具挑战性，因为相机的三个独特特征，包括极高的数据速率，有限视场（FOV）的方向传感特性以及视觉遮挡的存在。开展了一项综合研究，以解决scn提出的独特挑战，其中协作是关键。研究了协同处理的三个方面：1)在存在视觉遮挡的情况下进行覆盖估计，以提供足够的感知覆盖冗余，并在视觉覆盖统计混合相机节点和目标统计的情况下实现协作；2)聚类在基于图像比较的语义邻居选择算法形成的邻居节点之间调度有效的睡眠-觉醒模式，以实现更有效的协作；3)分布式优化，针对网络数据处理，涉及如何有效地从多个分布式传感器获得鲁棒和准确的集成结果，以应对具有挑战性的视觉任务，如目标检测，定位和人群跟踪。

Smart camera networks (SCNs) merge computer vision, distributedprocessing, and sensor network disciplines to solve problems inmulti-camera applications by providing valuable information throughdistributed sensing and collaborative in-network processing.Collaboration in sensor networks is necessary not only to compensatefor the processing, sensing, energy, and bandwidth limitations of eachsensor node but also to improve the accuracy and robustness of thenetwork. Collaborative processing in SCNs is more challenging than inconventional scalar sensor networks (SSNs) because of three uniquefeatures of cameras, including the extremely higher data rate, thedirectional sensing characteristics with limited field of view (FOV),and the existence of visual occlusion. An integrated research iscarried out to tackle the unique challenges presented by SCNs wherecollaboration is the key. Three aspects of collaborative processingare investigated, 1) coverage estimation in the presence of visualocclusions to provide adequate redundancy in sensing coverage, and toenable collaboration where the statistics of visual coverage blendsthe statistics of camera nodes and targets, 2) clustering toschedule an efficient sleep-wakeup pattern among neighbor nodes formedby image comparison-based semantic neighbor selection algorithm formore efficient collaboration, and 3) distributed optimization, forin-network data processing that concerns how to effectively obtainrobust and accurate integration results from multiple distributedsensors for challenging vision tasks like target detection,localization, and tracking in crowds.