부산대학교 도서관

A critical component for various interactive visual Internet of Things (IoT) applications is to reconstruct 3-D scenes from RGB images, i.e., 3-D visualization. When multiple cameras are involved, the visualization outcome mainly depends on the quality of input images, which carry correlated and complementary visual information from different camera perspectives. One main challenge to improve visualization performance is how to efficiently coordinate multiple cameras under complex environmental conditions. To overcome this challenge, we propose a situation-aware multicamera collaboration scheme based on the maximization of correlated information among different inputs. First, the information gain of a single camera is modeled by quantifying the effect of view direction, resolution and signal-to-noise ratio (SNR) on image quality. A spherical Gaussian is then designed to model the mutual information among neighboring viewpoints and further calculate the total correlated information of the camera group by considering their information redundancy and complementarity. An adaptive coarse-to-fine algorithm is proposed to maximize the correlated information, which achieves effective decision making of optimal multicamera collaboration strategy, including cameras’ location, direction, and focal length configurations. Simulation and realistic experiments demonstrate the accuracy of the correlated information model and the efficacy of the scheme to improve reconstruction quality.