Robust Auto-Calibration from Pedestrians

The knowledge of camera intrinsic and extrinsic parameters is useful, as it allows us to make world measurements. Unfortunately, calibration information is rarely available in video surveillance systems and it is difficult to obtain once the system is installed. Auto-calibrating cameras using moving objects (humans) has recently attracted a lot of interest. Two methods are proposed by Lv-Nevatia(2002) and Krahnstoever-Mendonca(2005). The inherent difficulty of the problem lies in the noise that is generally present in the data. We propose a robust and a general linear solution to the problem by adopting a formulation different from the existing methods. The uniqueness of formulation lies in recognizing two harmonic homologies present in the geometry obtained by observing pedestrians, and then using properties of these homologies to obtain linear constraints on the unknown camera parameters. Experiments with synthetic as well as on real data are presented - indicating the practicality of the proposed system.