A camera calibration method includes obtaining a plurality of images of surroundings of a vehicle captured by a plurality of cameras, setting a region of interest (ROI) in each of the images, detecting one or more feature points of the set ROIs, matching a first feature point of a first ROI and a second feature point of a second ROI based on the detected feature points, calculating a first bird-view coordinate of the first feature point and a second bird-view coordinate of the second feature point, and calibrating the cameras by adjusting an extrinsic parameter of each of the cameras based on an error between the first bird-view coordinate and the second bird-view coordinate.

An image correction device including: an image data acquisition part for acquiring the image data obtained by a camera; and a correction part for correcting the image data on the basis of a first difference that is a difference between a first image height obtained by light having entered the camera at a first incident angle and a first reference image height obtained by light having entered the camera at the first incident angle when the camera is free of manufacturing errors.

Examples disclosed herein involve a computing system configured to (i) obtain first image data captured by a first camera of a vehicle during a given period of operation of the vehicle, (ii) obtain second image data captured by a second camera of the vehicle during the given period of operation, (iii) based on the obtained first and second image data, determine (a) a candidate extrinsics transformation between the first camera and the second camera and (b) a candidate time offset between the first camera and the second camera, and (iv) based on (a) the candidate extrinsics transformation and (b) the candidate time offset, apply optimization to determine a combination of (a) an extrinsics transformation and (b) a time offset that minimizes a reprojection error in the first image data, where the reprojection error is defined based on a representation of at least one landmark that is included in both the first and second image data.

High precision calibration of external parameters is possible even when the surrounding road surface is configured from a plurality of flat surfaces. Specifically, provided is a calibration device comprising a camera to be mounted on a vehicle traveling on a road surface; a characteristic point extraction unit which extracts characteristic points from a captured image obtained by being captured with the camera; a tracking unit which tracks the characteristic points extracted with the characteristic point extraction unit; a storage unit which stores a trajectory of the characteristic points obtained by tracking the characteristic points; a road surface estimation unit which estimates a flat surface on which the vehicle is traveling by using the trajectory of the characteristic points; a calculation unit which calculates a calibration trajectory, which is a trajectory of the characteristic points, for use in calibration based on the estimated flat surface; and an external parameter estimation unit which executes calibration of an external parameter of the camera by using the calibration trajectory.

A method for estimating camera orientation relative to a ground surface. Line segments are detected from an image captured by a camera. A first virtual cube having three orthogonal vanishing points with a random 3D orientation is superimposed on to the image. The line segments of the image are classified grouped into 3D-directional groups. A second virtual cube is superimposed on to the image with an initial 3D orientation. An optimal 3D orientation of the second virtual cube is computed by iteratively changing the 3D orientation of the second virtual cube and measuring perpendicular distances of the three orthogonal vanishing points to the three line segment groups in each iteration starting with the initial 3D orientation, wherein the optimal 3D orientation of the second virtual cube being one that provides shortest perpendicular distances. Co-variances of the orthogonal vanishing points of the second virtual cube at the optimal orientation are computed. Ground orientation is computed from the second virtual cube at the optimal orientation.

An imager assembly including calibration functionality and a housing. An imager is disposed inside the housing. The imager includes a lens assembly. An electro-optic element is disposed on a wall of the housing and operable between a substantially clear condition and a substantially darkened condition. A light source directs light at the electro-optic element which redirects the light toward the lens assembly.

A set-up tool for aiding vehicle sensor calibrations having a structure with a first and second vehicle contact points configured to contact a vehicle and establish a calibration axis. A substantially vertically projected flat blade laser may be placed in a position perpendicular to the calibration axis and used to align the set-up tool with a center of the vehicle. Once centered, the laser ma be transitioned to project a laser line, in coordination with a protractor having a base-line parallel to the calibration axis (which is now substantially parallel to a transverse axis of the vehicle), at a discreet angle away from the center of the vehicle. A target may then be placed along the laser line to aid in the calibration of vehicle sensors.

An apparatus includes: a first camera configured to view an environment outside a vehicle; and a processing unit configured to receive images generated at different respective times by the first camera; wherein the processing unit is configured to identify objects in front of the vehicle based on the respective images generated at the different respective times, determine a distribution of the identified objects, and determine a region of interest based on the distribution of the identified objects in the respective images generated at the different respective times.

A system, method, and non-transitory computer-readable medium are provided for creating a freeform field of view mask for a camera in a vehicle to increase the field of view of the camera during operation of the vehicle. The camera captures an image of a portion of the vehicle in front of a calibration screen. A processor includes logic that applies a blurring filter to the captured image to create a blurred image, determines a freeform boundary of a field of view of the camera in the blurred image, creates a field of view mask based on the freeform boundary; and applies the field of view mask to the camera for maximizing the field of view of the camera during operation of the vehicle.

An illustrative example camera device includes a substrate and a sensor supported on the substrate. The sensor is configured to gather image information. A lens is situated near the sensor and an electroactive polymer selectively causes relative movement between the sensor and the lens.