Global Coverage Maximization in PTZ-Camera Networks Based on Visual Quality Assessment

In this paper, we propose a novel method to automatically configure a pan-tilt-zoom camera network in order to maximize coverage and visual quality in complex indoor environments. Based on a suitable modeling of cameras and environment, the optimization procedure determines the most appropriate camera position and settings to fulfill a given coverage objective. To achieve this goal, we use a particle swarm optimizer with an appropriate fitness function that takes into account a number of concurrent metrics and constraints. Furthermore, the solution is found working into a simulated environment obtained with the ray-tracing software. The proposed solution has been tested in various synthetic and real environments, taking into account the presence of obstacles and other constraints. We also simulated dynamic situations, such as cameras failures, to test the capability of fast camera network reconfiguration. For the performance evaluation, and in addition to the simulation in virtual scenes, we have also conducted a validation phase in real-world scenarios, where we assessed how the introduction on the optimal configuration improves the solution of some typical computer vision tasks.