Global and Local Coverage Maximization in Multi-camera Networks by Stochastic Optimization

In this paper we present a camera positioning and reconfiguration algorithm for complex indoor environments. The algorithm initially optimizes the global coverage of the selected environment in order to maximize the visibility on the entire area. Reconfiguration of the devices is then performed after the camera installation, in order locally optimize coverage according to the application requirements. Both initial coverage optimization and reconfiguration are achieved using Particle Swarm Optimization (PSO). The proposed solution has been validated in different setups, also taking into account occlusions and blocking introduced by the presence of obstacles. The achieved results confirm the viability of the approach in both positioning and reconfiguration, also in presence of considerably complex environmental geometries.