Optimal Multi-View Fusion of Object Locations

In surveillance applications, it is common to have multiple cameras observing targets exhibiting motion on a ground plane. Tracking and estimation of the location of a target on the plane becomes an important inference problem. In this paper, we study the problem of combining estimates of location obtained from multiple cameras. We model the relation between the uncertainty in the location estimation to the position and location of the camera with respect to the plane (which is encoded by a 2D projective transformation). This is addressed by a theoretical study of the properties of a random variable under a projective transformation and analysis of the geometric setting when the moments of the transformed random variable exist. In this context, we prove that ground plane tracking near the horizon line is often inaccurate. Using suitable approximations to compute the moments, a minimum variance estimator is designed to fuse the multi-camera location estimates. Finally, we present experimental results that illustrate the importance of such modeling in location estimation and tracking.