Constrained Multiple Planar Reconstruction for Automatic Camera Calibration of Intelligent Vehicles

In intelligent vehicles, extrinsic camera calibration is preferable to be conducted on a regular basis to deal with unpredictable mechanical changes or variations on weight load distribution. Specifically, high-precision extrinsic parameters between the camera coordinate and the world coordinate are essential to implement high-level functions in intelligent vehicles such as distance estimation and lane departure warning. However, conventional calibration methods, which solve a Perspective-n-Point problem, require laborious work to measure the positions of 3D points in the world coordinate. To reduce this inconvenience, this paper proposes an automatic camera calibration method based on 3D reconstruction. The main contribution of this paper is a novel reconstruction method to recover 3D points on planes perpendicular to the ground. The proposed method jointly optimizes reprojection errors of image features projected from multiple planar surfaces, and finally, it significantly reduces errors in camera extrinsic parameters. Experiments were conducted in synthetic simulation and real calibration environments to demonstrate the effectiveness of the proposed method.

A Tool for Annotating Homographies from Hockey Broadcast Video

In order to develop solutions for automatic ice rink localization from broadcast video, a dataset with ground truth homographies is required. Hockey broadcast video does not tend to provide camera parameters for each frame, which means that they must be gathered manually. A novel tool for collecting ground truth transforms through point correspondences between each frame and an overhead view of the ice rink is presented in this paper. Through collaboration with the users of the tool, we have added features to improve accuracy and efficiency, especially in frames with few lines on the playing surface visible. A dataset of 4,262 frames has been collected, which will be used for research into automatic camera calibration techniques.

A Hough-Space-Based Automatic Online Calibration Method for a Side-Rear-View Monitoring System

We propose an automatic camera calibration method for a side-rear-view monitoring system in natural driving environments. The proposed method assumes that the camera is always located near the surface of the vehicle so that it always shoots a part of the vehicle. This method utilizes photographed vehicle information because the captured vehicle always appears stationary in the image, regardless of the surrounding environment. The proposed algorithm detects the vehicle from the image and computes the similarity score between the detected vehicle and the previously stored vehicle model. Conventional online calibration methods use additional equipment or operate only in specific driving environments. On the contrary, the proposed method is advantageous because it can automatically calibrate camera-based monitoring systems in any driving environment without using additional equipment. The calibration range of the automatic calibration method was verified through simulations and evaluated both quantitatively and qualitatively through actual driving experiments.

CAMERA CALIBRATION USING MULTIPLE UNORDERED COPLANAR CHESSBOARDS

The now widely available and highly popular among non-expert users, particularly in the context of UAV photogrammetry, Structure-from-Motion (SfM) pipelines have also further renewed the interest in the issue of automatic camera calibration. The well-documented requirements for robust self-calibration cannot be always met, e.g. due to restrictions in time and cost, absence of ground control and image tilt, terrain morphology, unsuitable flight configuration etc.; hence, camera pre-calibration is frequently recommended. In this context, users often resort to flexible, user-friendly tools for camera calibration based on 2D coded patterns (primarily ordinary chessboards). Yet, the physical size of such patterns poses obvious limitations. This paper discusses the alternative of extending the size of the calibration object by using multiple unordered coplanar chessboards, which might accommodate much larger imaging distances. This is done initially by a detailed simulation to show that – in terms of geometry – this could be a viable alternative to single patterns. A first algorithmic implementation is then laid out, and results from real multi-pattern configurations, both ordered and unordered, are successfully compared. However, aspects of the proposed approach need to be further studied for its reliable practical employment.