Camera calibration for uneven terrains by observing pedestrians

TLDR: This work addresses the problem of calibrating multiple cameras, with an overlapping field of view (FoV), observing pedestrians in a scene walking on an uneven terrain by automatically estimated the infinite homography between the cameras by using the special geometric information obtained from observing pedestrians.

Abstract: A calibrated camera is essential for computer vision systems. The prime reason being that such a camera acts as an angle measuring device. Once the camera is calibrated, applications like 3D reconstruction or metrology or other applications requiring real world information from the video sequences can be envisioned. Motivated by this, we address the problem of calibrating multiple cameras, with an overlapping field of view (FoV), observing pedestrians in a scene walking on an uneven terrain. This problem of calibration from an uneven terrain has so far not been addressed in the vision community. We automatically estimated the infinite homography between the cameras by using the special geometric information obtained from observing pedestrians. This homography provides constraints on the intrinsic (or interior) camera parameters while also enabling us to estimate the extrinsic (or exterior) camera parameters. We test the proposed method on real as well as synthetic data; encouraging results demonstrate the applicability of the proposed method.