Online camera auto-calibration appliable to road surveillance

Abstract

Camera calibration is an essential prerequisite for road surveillance applications, which determines the accuracy of obtaining three-dimensional spatial information from surveillance video. The common practice for calibration is collecting the correspondences between the object points and their projections on surveillance, which usually needs to operate the calibrator manually. However, complex traffic and calibrator requirement limit the applicability of existing methods to road scenes. This paper proposes an online camera auto-calibration method for road surveillance to overcome the above problem. It constructs a large-scale virtual checkerboard adopting the road information from surveillance video, in which the structural size of the checkerboard can be easily obtained in advance because of the standardization for road design. The position coordinates of checkerboard corners are used for calibrating camera parameters, which is designed as a “coarse-to-fine” two-step procedure to recover the camera intrinsic and extrinsic parameters efficiently. Experimental results based on real datasets demonstrate that the proposed approach can accurately estimate camera parameters without manual involvement or additional information input. It achieves competitive effects on road surveillance auto-calibration while having lower requirements and computational costs than the automatic state-of-the-art.