An accurate extrinsic camera self-calibration method in non-overlapping camera sensor networks

Accurately extrinsic camera self-calibration, namely determining the positions and orientations of the networked cameras by themselves, is essential for many applications such as surveillance, intelligent environments and traffic monitoring. This paper describes an efficient, range-free and anchor-free method for self-calibrating the extrinsic parameters of the cameras in a non-overlapping camera sensor networks. The proposed method is based on the method proposed by Ali on the 2004's CVPR. Knowledge of the locations or angles, got from the assisted sensor (accelerometer or angular-accelerometer) installed on the moving object provide additional effective constraints on the optimization problem in order to compute the cameras' poses. Simulation results show that the iteration times, the calibration error, the volume of the data needed by the improved method are far less than the original method. The advantage of the method is that it can be applied even when the target takes sharp turns out of any camera's Field of View (FoV) with little steps.