Relative Position Estimation of Non-Overlapping Cameras

Abstract

We present an algorithm for the estimation of the relative camera position in a network of cameras with non-overlapping fields of view. The algorithm estimates the missing trajectory information in the unobserved areas of the multi-sensor configuration using both parametric and non-parametric algorithms. First, Kalman filtering is used to estimate the trajectories in the unobserved regions. Next, linear regression estimates the position of the target based upon the motion model generated from the measured positions in the field of view of each sensor. Finally, the relative orientation of the sensors is calculated using the observed and estimated target position from adjacent cameras. We demonstrate the algorithm on both synthetic and real data.