Camera Network Coverage Improving by Particle Swarm Optimization

This paper studies how to improve the field of view (FOV) coverage of a camera network. We focus on a special but practical scenario where the cameras are randomly scattered in a wide area and each camera may adjust its orientation but cannot move in any direction. We propose a particle swarm optimization (PSO) algorithm which can efficiently find an optimal orientation for each camera. By this optimization the total FOV coverage of the whole camera network is maximized. This new method can also deal with additional constraints, such as a variable region of interest (ROI) and possible occlusions in the ROI. The experiments showed that the proposed method has a much better performance and a wider application scope. It can be effectively applied in the design of any practical camera network.