NeTS: Small: Distributed Solutions to Smart Camera Networks

University Of Tennessee Knoxville, Knoxville TN

Investigators

Abstract

Smart camera networks (SCNs) merge computer vision, distributed processing, and sensor network disciplines to solve problems in multi-camera applications by providing valuable information through distributed sensing and collaborative in-network processing. Collaboration in sensor networks is necessary not only to compensate for the processing, sensing, energy, and bandwidth limitations of each sensor node but also to improve the accuracy and robustness of the network. Collaborative processing in SCNs is more challenging than in conventional scalar sensor networks (SSNs) because of three unique features of cameras, including the extremely higher data rate, the directional sensing characteristics with limited field of view (FOV), and the existence of visual occlusion. An integrated research is carried out to tackle the unique challenges presented by SCNs where collaboration is the key. Three aspects of collaborative processing are investigated, 1) coverage estimation in the presence of visual occlusions to provide adequate redundancy in sensing coverage, and to enable collaboration where the statistics of visual coverage blends the statistics of camera nodes and targets, 2) clustering to schedule an efficient sleep-wakeup pattern among neighbor nodes formed by image comparison-based semantic neighbor selection algorithm for more efficient collaboration, and 3) distributed optimization, for in-network data processing that concerns how to effectively obtain robust and accurate integration results from multiple distributed sensors for challenging vision tasks like target detection, localization, and tracking in crowds.