Vehicle localisation and deep model for automatic calibration of monocular camera in expressway scenes

Abstract

The authors present a fully automatic method for camera calibration in expressway scenes and vehicle localisation on curved roads. Our approach does not depend on specific targets or a priori information and adapts to a wide range of variations in road appearances and camera views. We make three main contributions to automatic camera calibration. Firstly, we propose a Deep Calibration Network to estimate the vanishing point and camera extrinsic parameters from an RGB image in an end-to-end manner. The vanishing point and camera rotation angles are then used to calculate the camera focal length according to the perspective projection geometry, thus constructing the complete camera matrix. Secondly, we introduce a Multi-View Camera Calibration Dataset, which contains a total of 32,712 images for 17 expressway scenes. The dataset covers a wide range of camera views and expressway types, which is challenging. Thirdly, we present a novel Curved Reference Line System adapted to the expressway shape to locate vehicles on curved roads accurately. This coordinate system can effectively transform the vehicle coordinates from a Cartesian system to the vehicle's mileage and horizontal distance relative to the lane line. A comprehensive evaluation on Multi-View Camera Calibration Dataset shows that our technique can automatically calibrate a single camera at about 40fps on an NVIDIA GTX1060 processor and accurately locate vehicles on curved roads, making it suitable for deployment in expressway surveillance systems.