Localizing non-overlapping surveillance cameras under the L-Infinity norm

This paper presents a new approach to the problem of camera localization with non-overlapping camera views, particularly relevant for video surveillance systems. We show how to recast localization as quasi-convex optimization under the L-Infinity norm. Thereby we add the problem of reconstructing camera centers and 3D points for non-overlapping cameras with known internal parameters and known rotations to the class of known geometric problems solvable with Second Order Cone Programming. The 3D points are never seen by more than one camera, which makes the localization problem ill-posed. Therefore, the proposed approach employs temporal consistency of the 3D points to supply the missing constraints. Our formulation allows a global optimal solution to be found with a clear physical meaning of the cost function being minimized.