NeTS: Small: Distributed Solutions to Smart Camera Networks

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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.