Robust Auto-Calibration using Fundamental Matrices Induced by 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 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 were 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 our formulation lies in recognizing two fundamental matrices present in the geometry obtained by observing pedestrians, and then using their properties to impose linear constraints on the unknown camera parameters. Experiments with synthetic as well as real data are presented -indicating the practicality of the proposed system.