A trailer angle identification system comprises an imaging device configured to capture an image. An angle sensor is configured to measure a first angle of the trailer relative to a vehicle. A controller is configured to process the image in a neural network and estimate a second angle of the trailer relative to the vehicle based on the image. The controller is further configured to train the neural network based on a difference between the first angle and the second angle.

A method of tracking a mobile device comprising at least one camera in a real environment comprises the steps of receiving image information associated with at least one image captured by the at least one camera, generating a first geometrical model of at least part of the real environment based on environmental data or mobile system state data acquired in an acquisition process by at least one sensor of a mobile system, which is different from the mobile device, and performing a tracking process based on the image information associated with the at least one image and at least partially according to the first geometrical model, wherein the tracking process determines at least one parameter of a pose of the mobile device relative to the real environment. The invention is also related to a method of generating a geometrical model of at least part of a real environment using image information from at least one camera of a mobile device

A vehicular vision system includes a plurality of cameras and a processor operable to process image data captured by the cameras to generate images derived from image data captured by at least some of the cameras. A display screen, viewable by a driver of the vehicle, displays the generated images and a three dimensional vehicle representation as would be viewed from a virtual camera viewpoint exterior to and higher than the vehicle itself. A portion of the displayed vehicle representation may be at least partially transparent to enable viewing at the display screen of an object present exterior of the vehicle that would otherwise be partially hidden by non-transparent display of that portion of the vehicle representation. The three dimensional representation may include a vector model without solid surfaces, or may include a shape, body type, body style and/or color corresponding to that of the actual vehicle.

An image processing device includes a marker detector configured to detect markers including white lines extending in two directions on a road surface based on an image signal from an imager that takes an image of the road surface around a vehicle, a parking frame detector configured to compute adjacent markers on the road surface among the detected markers, and to detect a parking frame defined by the adjacent markers based on a distance between the adjacent markers, and a shape estimator configured to detect extending directions of the white lines of the markers that are included in the detected parking frame, and to estimate a shape of the parking frame based on the extending directions of the detected white lines.

A parking position identification method includes acquiring input data as an image that is generated by photographing a parking region by a camera which is installed in a target vehicle, and identifying a parking position of the target vehicle in the photographed parking region by inputting the input data to a learning model that indicates a relationship between the parking region which has a width in which parking of at least one vehicle is feasible and a parking position for one vehicle in the parking region.

A parked-vehicle detecting unit (12c) detects a parked vehicle in a parking lot, using an image of the parking lot viewed from above. An empty space detecting unit (12d) detects, as an empty space, an area in the parking lot that does not have the parked vehicle detected by the parked-vehicle detecting unit (12c) and that is determined to be larger than a target vehicle to be guided. Then, an information generating unit (12g) generates notification information indicating the empty space detected by the empty space detecting unit (12d).

A camera parameter set calculation apparatus calculates three-dimensional coordinate sets corresponding to overlapping regions including images of a portion of a subject, based on a first and second images captured by a first and second cameras, a first camera parameter set of the first camera and a second camera parameter set of the second camera; determines first pixel coordinate pairs based on the first camera parameter set by projecting the three-dimensional coordinate sets on the first image; determines second pixel coordinate pairs based on the second camera parameter set by projecting the three-dimensional coordinate sets on the second image; calculates an evaluation value, based on pixel values at the first pixel coordinate pairs on the first image and pixel values at the second pixel coordinate pairs on the second image; and updates the first and second camera parameter sets based on the evaluation value.

In a system and a method for extrinsic calibration of an image capture system of a vehicle, the vehicle includes a body and a body portion, wherein the body portion is configured to rotate around a rotation axis relative to the vehicle. The system includes an image capture system with an image capture device mounted on the body portion and adapted to capture at least two images of the body and/or surrounding area of the vehicle, an identification unit adapted to identify at least two image features in the images, a calculation unit adapted to calculate a direction of the rotation axis relative to the image capture device based on the image features, and a calibration unit configured to determine extrinsic parameters of the image capture system based on the calculated direction of the rotation axis relative to the vehicle. The image capture system is calibrated using degrees of freedom of movement that the image capture device has due to being mounted on a movable portion of the vehicle.

Techniques including receiving a distorted image from a camera disposed about a vehicle, detecting, in the distorted image, corner points associated with a target object, mapping the corner points to a distortion corrected domain based on one or more camera parameters, mapping the corner points and lines between the corner points back to a distorted domain based on the camera parameters, interpolating one or more intermediate points to generate lines between the corner points in the distortion corrected domain mapping the corner points and the lines between the corner points back to a distorted domain based on the camera parameters, and adjusting a direction of travel of the vehicle based on the located target object.

The disclosure relates to methods, systems, and apparatuses for virtual sensor data generation and more particularly relates to generation of virtual sensor data for training and testing models or algorithms to detect objects or obstacles. A method for generating virtual sensor data includes simulating, using one or more processors, a three-dimensional (3D) environment comprising one or more virtual objects. The method includes generating, using one or more processors, virtual sensor data for a plurality of positions of one or more sensors within the 3D environment. The method includes determining, using one or more processors, virtual ground truth corresponding to each of the plurality of positions, wherein the ground truth comprises a dimension or parameter of the one or more virtual objects. The method includes storing and associating the virtual sensor data and the virtual ground truth using one or more processors.