Auto-focusing in Extreme Zoom Surveillance: A System Approach with Application to Faces

Auto-focusing is an indispensable function for imaging systems used in surveillance and object tracking. In this paper, we conduct a study of an image-based passive auto-focusing control for high magnification (>50×) systems using off-the-shelf telescopes and digital camcorders with applications to long range near-ground surveillance and face tracking. Considering both speed of convergence and robustness to image degradations induced by high system magnifications and long observation distances, we introduce an auto-focusing mechanism suitable for such applications, including hardware design and algorithm development. We focus on the derivation of the transition criteria following maximum likelihood (ML) estimation for the selection of adaptive step sizes and the use of sharpness measures for the proper evaluation of high magnification images. The efficiency of the proposed system is demonstrated in real-time auto-focusing and tracking of faces from distances of 50m~300m.